The key to outpacing your competition in a dynamic marketplace is to equip your decision-makers with the best quality insights at the optimal point of impact.
You look to us for guidance on the best analytics solutions and technologies—as well as the best strategies to implement those solutions for maximum benefit. Part of that is to be transparent with you on our product roadmap to help you achieve the outcomes you require today and gain insight into where you can go in the future. The Oracle Analytics product roadmap is now published, and ready for your review.
At Oracle, we strive to provide an analytics solution that powers actions driven by deep insights from all available data. To achieve this goal, Oracle Analytics is focused on serving all of our customers’ analytics needs, from simple to advanced. Our product roadmap is organized into three key investment areas:
Oracle powers deeper insights by embedding machine learning and AI into every aspect of the analytics process. We apply capabilities like natural-language processing (NLP) and natural-language generation (NLG) for better conversation analytics, and we employ smart data preparation and discovery to incorporate customer-specific reference data. Investing more in augmented analytics now is essential to exceeding your expectations in the future.
We provide a complete, end-to-end self-service environment that goes far beyond data visualization. Tools like interactive visualization capabilities and a unified dashboard experience put rich data and modeling capabilities into the hands of almost any user. Exploiting all your data—both governed and personal—gives you the analytical freedom you want within a framework of sanctioned corporate data you need.
Oracle lets you scale analytics with a secure, extensible architecture that can be personalized. Prebuilt analytics data models help you accelerate application deployment time, and enterprise-class architecture and security models enhance the solution’s functionality and connectivity. These requirements don’t go away but are an increasingly important part of any enterprise analytics system.
Our competitors’ offerings force customers to compromise between governed, centralized, and self-service analytics. Oracle provides a single solution that blends these requirements and incorporates machine learning (ML) and artificial intelligence (AI)—augmented analytics—into every step of the process. Check out the new Oracle Analytics product roadmap today and explore the endless possibilities of world-class business analytics—both now and into the future.
I’d love to hear any feedback you have. Please comment below or contact me at firstname.lastname@example.org.
Stay ahead of your competition with Oracle Analytics Cloud. We give executives and analysts the ability to understand critical metrics, what is driving trends, and the potential impact of changing processes. Sign up for a trial and begin your journey into Oracle Analytics Cloud today.
Sammy Singh (also known as Sam Singh Mehta) founder of CFO Base , Atmosify Shakespeareo…
Ireland’s main postal provider and largest retail network, An Post, serves 2.1 million homes through 4,000 delivery routes where it transports 2 million pieces of mail per day.
The national carrier service also includes overnight data load processes, continuous package monitoring, and data quality controls.
Additionally, An Post supports a retail network, where every week customers pay for a number of services ranging from bill payments, social welfare payment pickup, and its own retail banking services. An Post recently added foreign currency exchange to its retail services, moving to 35 percent of market share in a short period of time.
With the help of Oracle Analytics Cloud, An Post takes in data from various sources including finance, manpower management , parcels and mail operations, retail. The analysis is provided to customers through interactive self-service dashboards as well as to An Post staff in mail processing plants nationwide.
Security remains a concern. And as John Cronin, group CIO for An Post explains it, postal and banking fraud is devastating for customers. To prevent even the opportunity for fraud to occur, An Post is using Oracle Analytics Cloud to spot issues and alert customers to the problem faster than ever before.
“We know the future is in artificial intelligence and machine learning,” Cronin says. “An Post will not be
found wanting in using its Oracle implementation. We’ve invested heavily in this as the strategic solution for Analytics and , we’re going to enhance it, grow it, and do more with it. And the immediate future and strategic focus is machine learning and AI”
The anti-fraud system triggers alerts into a central office when there’s suspicion, based on the algorithms, of fraud happening. The benefit of a centralized notification system, says Cronin, is that investigations are handled immediately instead of three weeks after the event, “when you’re trying to figure out, was there fraud or not.”
“Being able to readily check out an office where there maybe suspicious activities leading to fraud happening is a major advancement of the Oracle Analytics Solution.” Cronin adds.
For companies like An Post, customer experience is a top priority. Having an analytics platform that can provide quick insights into the retail space around cash accounting and cash management as well as customer protections is also important for success.
When asked what his advice would be for any enterprise on the fence about using a cloud-based analytics platform, Cronin doesn’t mince words. “If I’m talking to another business that hasn’t gone into the analytics world, they need to go there and fast, and they should to talk to Oracle, would be my advice. And why? They’re good,” Cronin says.
In the video below, Cronin shares the rest of his view of the future and details how Oracle Analytics Cloud is making a huge impact for An Post and its global corporate customers.
An Post is just one of Oracle’s partners using Oracle Analytics Cloud to support its customers’ digital transformations and cloud-based endeavors.
You can stay ahead of your competition with Oracle Analytics Cloud by giving executives and analysts the ability to understand critical metrics, what is driving trends, and the potential impact of changing processes. Sign up for a trial and begin your journey into Oracle Analytics Cloud today.
Building a report or dashboard can be easy when the data comes from a simple spreadsheet and there are a limited range of simple queries. But what happens when your data collection comes from multiple sources and you want to identify very complex concepts? You are better off using software with a data aggregation rule that can handle the job.
Oracle Analytics Cloud data visualization feature supports different aggregation rules for any metric like “sum,” “average,” “minimum,” or “maximum.” The rule defines how the metric will aggregate when queries return data at an aggregated level. For example, a business manager may ask, “What is the sum of sales by customer when source data is at the sales order level?”
A metric’s aggregation rule can be edited in several places while building an analysis in Oracle Analytics Cloud. It can be changed at a “metric” property level via different dialog boxes, or it can be changed in a specific visualization, for only that dashboard.
If you’ve ever wondered about the difference in behavior between these two scenarios, let’s dig deeper and find out.
1) Setting aggregation at a metric level
This can be done in two ways: in the “prepare” tab or in the “visualize” tab when building a data visualization project. The resulting behavior is the same in both cases; it sets the default rule for aggregation of the metric in this specific dataset. So, this will establish the default aggregation rule for any project using this dataset column, not just for the project we are working on.
Setting it directly from the prepare tab:
To set a metric aggregation in the “prepare” tab, click the “metric” and in the properties pane, go to “general” tab and change the aggregation to a desired option.
Once you apply this change, the new aggregation method applies to all the visualizations that use this metric across different data visualization projects. For example, if we set the aggregation of a sales metric to “sum,” every time this metric is used in a visualization, the value is computed using “sum” aggregation. Let’s look at the Sales by Product Category below:
Now that the property is set for this metric, the dataset level for every project or visualization using this metric will compute following the new aggregation rule. To check that, when we inspect the dataset from the dataset pane and look at its elements, we see the metric aggregation is set to “sum.”
Setting the aggregation rule from the data visualization canvas (visualize tab):
Another option to set the same level of aggregation is to change it directly from a metric column in the data visualization canvas (using the visualize tab as opposed to the prepare tab). To set a metric aggregation from there, simply click on the metric among the data elements and change the aggregation rule in the properties pane (as seen in the bottom left pane below).
The effect here is the same as setting it in the prepare tab. It will set it as the default aggregation for that metric and will be used every time the metric value is computed, in any project.
2) Setting aggregation at a visualization level
Metric aggregation can also be set for a single visualization in a given data visualization project only. In this case, the setting is specific to the visualization and overrides the default aggregation rule set at the dataset level. This can be done by clicking on any given visualization that exists on your canvas, going to the values sub-tab within the properties pane (the # sign) and setting the aggregation method there. If a visualization has multiple metrics, each metric can have a different aggregation method. Changing the rule there will only apply to the visualization that you edited and will not impact the dataset. So, any other visualization using the same metric, in this project or any other project, will keep the original aggregation rule.
At first sight, changing the aggregation method at the visualization level may seem of trivial use, but in fact it can be quite useful.. If the visualization has a total invoked in it, the aggregation to arrive at the total value will be the one specified in the visualization property. For example, let’s look at the table of “Sales by Products Category” with default aggregation “sum” for this column and add a total to the visualization. By default, the total value is calculated as a “Sum of Sales.” If we change the aggregation method only for the visualization to “average,” we will now see the total of the report showing an “Average of Product Categories Sales.”
Note that this only changed the total aggregation line; the value for lines did not change. Why? The calculation done by Oracle Analytics Cloud here includes two passes. The first pass retrieves sales by each dimension requested in the visualization, in this case product category. This retrieving of data is still done using the default dataset aggregation rule (“sum” in our example). The second pass, at the visualization level, aggregates the retrieved data using the specific visualization rule for each row in the visualization. In this case, it simply shows the average of sales for each product category and also for the total of the report. For each single product category, the average total of sales is the same as total sales, but for the sum of all product categories, we get the average value of product category sales.
However, if you look closer at the options, there is a very powerful subtlety that can be leveraged when using visualization level aggregation rules: the “by” clause option.
Let’s keep using our table of sales by product categories, and let’s now say we want to see average monthly sales for each product category. For each product category, we want average of sales by month. That’s what the “by” clause will let us achieve.
As we set the visualization level aggregation method to “average,” we can click on the “by” field and set it to any set of attributes (one or many). Let’s just pick order month for our example. The view now shows monthly average sales for each product category and this aggregation is specific to the visualization.
In this case, Oracle Analytics Cloud retrieved sales information, summed it by product category and order month in the first pass, and then, for each product category, calculated the average of all the monthly sales values. This applies to any other aggregation, with the “by” columns of your choice, and for any data visualization. This is a pretty powerful analytics calculation done by a single user-friendly click.
Now that you have mastered this aspect of data visualization, visit our website and begin the rest of your journey into Oracle Analytics Cloud.
Unveiling its latest cohort, Alchemist announces $4 million in funding for its enterprise accelerator
Growing at an exponential rate, data continues to surge to unprecedented new levels. Every day, 2.5 quintillion bytes of new data are created. And more than 90 percent of the world’s data has been generated during the past three years. While business intelligence (BI) systems have been in existence for a while, the time has now come for organizations to overcome the shortfalls of legacy BI systems with more robust systems of insight.
Data is flooding in from various sources—mobile, computers, servers, wearables, and IoT devices—leading to a rise in unstructured data. Add to that the changing regulations, and most businesses are unable to process and analyze all the information that is available to them. As a result, they often make critical decisions relying only on subsets of data and miss out on potential growth opportunities.
The future of analytics is self-service access to intelligent insights. By leveraging embedded machine learning and integrating analytics, intelligent insights present the right information to the right people, at the right time, using a device of choice.
Existing BI systems need to go through a dramatic change to keep up with these changing requirements. Read this Forrester report, which recommends that application development and delivery (AD&D) professionals focus on empowering the end customer—the business user—and help enterprises become more insights-driven.
Earlier Generation BI Is No Longer Enough
BI systems have been going through an evolution, and Forrester estimates that older BI environments can process and analyze only 20 percent of all data available in an average enterprise. Modern SQL databases impose significant overhead, and they have not been able to keep up with shifting customer behavior and changing regulations that require a technology that works with unstructured data.
There are also limitations in the waterfall design, which ignores hidden patterns and insights. And finally, priorities for business users and technologists are not always aligned. So new BI solutions now focus on empowering business users, and big data permits organizations to get a complete 360-degree view of their customers.
Start a New Chapter, Focus on Customer Insights
Forrester suggests that AD&D professionals and business leaders focus on their end customer. This includes:
- Encouraging your business peers to own systems of insight and embed analytics in every business, revenue-generating department so that other C-levels such as the CMO, COO, and CFO own their system of insight, while the CIO and AD&D play a supporting role.
- Enabling an insights architecture. Instead of focusing on building reports, Forrester suggests that you empower business team members with self-service capabilities and manage your BI requirements while deriving the required insights and sharing expertise with the rest of the organization.
Oracle Analytics Cloud offers the ability to create data flows and perform incremental loads on a target table. Data flows can operate only on the incremental data which becomes available in the source in between the current run and the previous run.
In this blog, let’s see how to perform an incremental load on a database table. The video below walks you through the configuration process and how you can define a method of continuing data processing from the last processed row. The rest of the blog will help you with the step-by-step instructions.
Prerequisites for Performing Incremental Loads
Consider the following before attempting this sequence:
- Incremental loads can be performed only on those data sets where the source and target tables are database-based.
- If there are multiple data sets in a data flow, then only one of the sources can be set for an incremental update.
Defining a Source and New Data Identifier for the Source
The first step is to have a source table and identify a column using new data that can be identified in the table. In this example, I have a revenue fact table with month key as a new data identifier
The next step is to create the Oracle Analytics data source pointing to this table. In the process of creating the data source, make sure you mark the new identifier column by clicking on the third node in the process flow. This is an important step as this column defines how the system will be able to identify new rows in the dataset.
Define a Data Flow
Now that our data source is created, let’s define a data flow by importing the revenue tact table from the source connection. The key here is to check the “Add new data only” box to ensure that the source table is marked for incremental load.
To make my data flow a bit more functionally representative, I will add a business example, such as converting currency values. Let’s bring in a spreadsheet which has exchange rates for every month to convert, and let’s join it based on the month key column. Let’s add a new calculation to convert revenue.
Finally, let’s select step “Save Data” and specify a name for the resulting data set. Make sure you choose the target connection as a database and specify the table name where the result set needs to be saved. There are two options available to select in the “When Run” drop-down menu.
- “Replace existing data” makes the data flow truncate the target table and reload all the records.
- “Add new data to existing data” keeps the existing records intact and loads only the new records in the source table. New records are identified by the column we defined in the source dataset above.
Let’s set the “When Run” option to “Add new data to existing data” and save the data flow.
Now, let’s run the data flow for the first time. As it completes, we can see in our underlying database that the target table has been created. Since this was the first run of the data flow, all the records in the source table have been inserted into the target table.
Now, for the example in this blog, let’s go and delete a month of data (201812) from our newly created target table. After doing this, our source table still has its 12 months of data (Jan to Dec) but our target table now only has 11 months; it is missing December. Notice that we did not change data in our source table, so there are no new records there since our last run of the data flow.
So, as we run the data flow for the second time, the target table does not get incremented at all. The data flow was set to only bring across new data, but there is no new data in the source, so nothing is changed in the target table. We can check that the target table is still not loaded with the deleted month’s data. If the data flow had been set to full load, all the data would be in the target.
Now, to complete the test, let’s manually load our source table with three more months of data. This will represent some incremental data. Then let’s rerun the data flow once again. We will see that the target table has been incremented with the three new months of data coming from the source table. But notice that data in the target table is still missing for the month where the records were deleted:
Remember what we said at the beginning of this blog: If we go back to the data flow definition and set the Run option to “Replace Existing Data” in the target table, then when we run the data flow, all the data gets loaded, including the deleted month’s data.
If you’re seeking to build a more insight-driven organization, you’re probably looking for ways to extend and improve your business analytics. There are plenty of products on the market today, but the one you choose will determine just how well—and how quickly—your organization will be able to create value from data. To be effective, the solution must be able to support existing use cases while also allowing your users to connect, enrich, combine, and visualize data on their own.
According to Stewart Bryson, CEO and founder of Red Pill Analytics, that’s where Oracle Analytics Cloud stands out from other cloud-based data analytics products.
“With Oracle Analytics Cloud, you get best-of-breed data visualization and cloud connectivity while still being able to satisfy your enterprise use cases—all in a single solution,” Bryson says. “Other offerings, such as Microsoft Power BI, require you to purchase multiple products to satisfy enterprise use cases and perform just-in-time modeling. And even after you purchase those products, you might need to spend time and resources on configuration and integration before you’ll realize any value from your investment.”
As Bryson explains, “Oracle Analytics Cloud is built to address all the ways in which organizations interact with their data, and it requires minimal configuration compared to Microsoft Power BI. Oracle Analytics Cloud lets you connect to all Oracle SaaS offerings without additional tools—or extra work. The self-service data visualization capability is a quality solution from a development perspective, providing support for both cloud data services and the curated enterprise catalog that companies are used to. And Oracle Analytics Cloud lets you mash up between those two use cases so you can prototype how new datasets from nontraditional sources might look alongside, or even joined to, enterprise use cases.”
Bryson believes that this capability is truly unique, providing all the requirements and discovery processes businesses need, together within one tool.
Accelerating Your Time to Value
Having all of these rich capabilities available in a single solution provides Red Pill Analytics customers with immediate value in terms of time saved.
“Other solutions usually require you to implement and integrate multiple components,” Bryson says, “but you can stand up Oracle Analytics Cloud in just a few clicks. And the service is very easy to provision, so you get faster time to value compared to Microsoft Power BI and other products.
“Oracle Analytics Cloud gives you the freedom to stand up a variety of use cases and new models,” he adds. “You can spin up one service as a production environment, another for development, another for test, and other Oracle Analytics Cloud services for proofs of concept. This gives you the opportunity to test theories and use cases outside of production, quickly and easily. Trying new things in a protected environment in this way helps you get right to the analytics that bring value to your company. Oracle Analytics Cloud is designed for organizations that want to be data-driven, not infrastructure-driven.”
Elastic Scaling Without Heavy Lifting
Bryson is certain that Oracle Analytics Cloud is also a step ahead in its ability to scale up or scale down to adjust resources for the changing nature of workloads. Paired with Oracle Autonomous Data Warehouse, which is an easy-to-use, fully autonomous database cloud service, it dramatically reduces the time and money needed to scale. “You can scale elastically when you need to, without spending a large chunk of time on capacity planning. And you can easily extend multiple Oracle Analytics Cloud services for different departments or organizations or stand up one large service for multiple organizations. At the data tier and at the analytics tier, it’s easy to take a snapshot of what you have and increase the size of the service as often as you want,” Bryson says.
The Complete Solution for Cloud-Based Analytics
Finally, Bryson told me that Oracle Analytics Cloud is a great tool even if you aren’t a full Oracle shop—it still brings significant and unique value. “With Oracle Analytics Cloud, you have the freedom to use other analytics solutions that you’ve already invested in—without needing to create an Oracle silo.”
When it comes to agility, scalability, and support for innovation, the Red Pill Analytics CEO knows from experience that competitive offerings like Microsoft Power BI simply come up short. “There’s no question that Oracle Analytics Cloud offers the most complete solution for cloud-based business analytics,” Bryson says.
For more information on how Oracle Analytics compares to Power BI, check out this white paper. To find out more about how analytics can benefit your enterprise, visit our website and begin your journey into Oracle Analytics.
The biggest extravaganza in the world of cricket—the ICC Cricket World Cup—is upon us. The tournament is one of the world’s most viewed sporting events and is considered the “flagship event of the international cricket calendar” by the International Cricket Council. This is the 12th edition of the Cricket World Cup scheduled to be hosted by England and Wales.
For those not familiar with the game, cricket is played on a large field (or pitch) with a running area bound on either side by two wickets and six stumps. A bowler (someone who throws the ball) hurls the cricket ball while a batter (with a large flat-faced cricket bat) tries to hit it past a sea of fielders. Teams take turns at both offense (batting) or defense (bowling) based on a side out.
There are many factors that can influence a cricket match outcome:
- The weather on the day of the match dictates whether it ends up being a high scoring or a low scoring affair.
- The condition of the pitch can also play an important role in the outcome.
- The skill of the players can be a key factor.
- The toss… who doesn’t dread losing the toss?
While there are several factors influencing the outcome, one of the most difficult decisions that captains must make is whether to bat first or bowl first. This is a crucial decision and a strongly debated topic. One way to decide is to look at history: How did teams fare batting first vs. chasing, and how did this pattern vary across venues.
Can analytics help in making this decision?
Following are very simple Oracle Analytics Cloud (OAC) visualizations that look at all the world cup matches played from 1975 to 2015. Using OAC, it only takes a few minutes to gain interesting insights on a strategy to adopt for teams competing at the 2019 World Cup.
Eleven World Cups have been played so far, and England hosted four (4) of them. Australia won the World Cup 5 times; India and West Indies come in a distant second by winning two each; Sri Lanka and Pakistan won it only once.
A Marker in Finalists’ Strategy
Teams batting first won 63 percent of the time, while teams chasing first only won 37 percent of the time. So, if a team is skilled enough to enter the finals, batting first doubles its chances of holding that cup!
Team Strategies That Worked
Digging a bit deeper, this pattern varies by teams. For Australia and India, batting first or second didn’t seem to matter when they won the World Cup. West Indies, however, only won by batting first. So, if West Indies makes it to the final at Lords this time around, the best bet would be batting first.
Team Strategy When England Is Hosting
Since England is the host nation in the upcoming event, let’s shift our focus to only those World Cups which were played in England. Interestingly, when hosted in England, finals were won most of the time (75 percent) by teams batting first.
When it comes to semifinals, it’s the opposite, with teams batting second having won most semifinals.
These numbers contrast significantly with semifinals and finals hosted outside of England. In all the other countries, teams batting first have won most finals and semifinals.
Could venue even play a part in this pattern? Let’s consider the premier venues of England where most of the games in the 2019 World Cup are about to be played: Birmingham, Leeds, and Lords. Teams playing there have done equally well batting first or second.
But if you are playing in Manchester (where the 2019 semifinals will be played), history suggests it’s better to chase, while at The Oval (London), teams batting first have fared way better.
Let’s look at the venues where teams have won with the highest average runs margin. Birmingham, Taunton, and Chester Le Street are the preferred grounds to bat first where the winning margin is high.
Now let’s look at those venues where teams bowling first have won with the highest wickets margin. Canterbury, Lords, and Leeds are the venues where teams chasing first have fared well.
Looking at overall batting win rate across premier venues in England, Nottingham and The Oval are the most preferred venues to bat first.
Team Strategy Across World Cups Games
Some individual teams historically fare better when batting first vs. chasing at World Cup matches, irrespective of venues and location. Australia, the top performer, has mostly won by batting first (60 percent). In contrast, arch rivals New Zealand are way better chasers (60 percent).
In the early days of World Cups or even other cricket matches, for that matter, teams always preferred to bat first. Bat first, score big, and put the opposition under pressure. But looking at the trend from the 2007 World Cup onwards, stats show us that there is not much of a difference in batting first or chasing. Does this indicate that teams are getting better playing under pressure while chasing? Perhaps they are.
Most of the cricket enthusiasts would argue that batting first has been the best winning strategy so far, but data does not always support that position: captains winning the toss would benefit from looking at historical stats to make an informed decision.
For the upcoming World Cup in England, insights from historical data suggest a few distinct approaches:
Semi Final 1 (in Manchester): Data shows that Manchester has been a venue where teams have dominated while chasing. It also shows that historically in England, semifinals were won by chasing. So, the team winning the toss is more likely to win the match if they let the opposition bat first.
Semi Final 2 (in Birmingham): If you win the toss in Birmingham, though the venue doesn’t favor teams batting first or bowling, data also suggests that there have been big wins with runs margin (i.e., by batting first). The team winning the toss is more likely to win the match if they bat first.
Finals (Lord’s): Historically, teams have won in finals in England by batting first. The team winning the toss is more likely to take the game if they bat first.
May the best team win!
Google expands digital well-being tools to include a new ‘Focus mode,’ adds improved parental controls to Android
Cloud-based business analytics is vital for today’s organizations but finding the right business analytics solution can be a challenge. Today’s enterprises are becoming increasingly complex. If you want to gain real insight into what’s happening, you need tools that do more than simply show pictures of narrow sets of data.
According to Matt Yorke, group director of Qubix International, a truly effective solution will take you beyond basic desktop visualization and include capabilities to address all aspects of your analytic needs. And it should let you do that with a single, comprehensive platform, instead of forcing you to spend time creating custom processes that require multiple separate tools—and nonstop refinement to enrich, combine, augment, visualize, and collaborate on key insights.
A Complete Platform for Business Analytics
“Oracle Analytics Cloud provides that complete platform,” Yorke says. “With Oracle, you get an entire portfolio of capabilities, which is a big advantage over products like Tableau that are much more limited in scope. Like Tableau, Oracle Analytics Cloud provides rich visualization capabilities, which are dynamic and easy to use, and it lets you import from and connect to many different data sources.”
Yorke told me that visualization is just one of the capabilities that sets Oracle Analytics Cloud apart for him. From his experience, Oracle also offers advanced business intelligence (BI) that others can’t match. “With Oracle Analytics Cloud, you can use a common enterprise data virtualization model as the foundation for business insight,” he says. “It employs prebuilt data virtualization that abstracts physical data sources from the business views. All data comes from the same data virtualization layer to provide a single source of truth. In Tableau, the business owns the semantic layers, which can result in loss of control and governance by IT.”
The Qubix director believes that Oracle also stands apart from other products in areas like reporting. “The reporting capabilities in Oracle Analytics Cloud allow you to make high-volume, high-velocity reporting available directly to users, on demand or automatically,” he says. “And Oracle Essbase provides lightning-fast reporting, scenario modeling, planning, and what-if capabilities that are unmatched in the market today. You can even batch burst reports out to remote areas, to deliver them to store managers or other branch locations. You won’t find these capabilities in Tableau.”
Data Visualization Across the Enterprise
Oracle Analytics Cloud stands out from the pack not only in its breadth of features, but in its view of the customer’s world. As Yorke explains, “Tableau works well at providing insight at the departmental level, but there is no single version of the truth because each department has its own, specific view of the business. A compartmentalized view also creates limitations because it cannot scale to the enterprise. Tableau has a limited ability to scale out, and it doesn’t work well for management reporting where you need the enterprise-level view.”
Oracle Analytics Cloud provides a department-level entry point, but it lets you do much more. “You can easily scale and add more data, integrate more departments, and eliminate duplication of effort and silos of information—all to develop that single source of truth,” Yorke says.
As a pure business analytics platform, Oracle Analytics Cloud gives you plenty of choices and options for sourcing your data from wherever it resides. The platform uses the Oracle Autonomous Data Warehouse, a proven technology that learns as it goes and can improve performance by tuning and optimizing its own engine. Instead of worrying about how to get data that you can query, you can focus on what you want to learn from your data. Oracle helps ensure that, at any scale, if you’ve got good quality data, you can access it—regardless of its location.
“In contrast, Tableau is a very singular, compartmentalized solution that focuses mostly on visualization,” Yorke says. “It doesn’t orchestrate the data, and it requires other products to acquire the data and synthesize and store it.” If you’re looking for the best solution to drive the flow of end-to-end insights, with no need for add-ons or customization, Oracle Analytics Cloud is the obvious choice.
To find out more about how analytics can benefit your enterprise, visit our website and begin your journey into Oracle Analytics Cloud.
Customer loyalty is everything in the world of consumer-packaged goods (CPG). Products can fly off the shelves or sit unsold for months based on the needs and perceptions of a fickle audience. To keep ahead of the competition, many CPG companies have turned to a coordinated analytics strategy.
The stakes for CPG companies could not be higher. Industry sales in the United States alone are expected to grow to $721.8 billion in 2020 up from the $635.8 billion recorded in 2015, according to compiled data at Statista.com. Much of the growth comes from emerging market economies and the subsequent increase in global consumption.
One tactic in gaining market share and mind share is the use of analytics in customer clustering. More advanced than market segmentation, manufacturers and retailers cluster data into units with similar behaviors that may be managed together to predict desired outcomes.
To help make sense of the impact of data analytics on the CPG industry, we invited Ashish Joshi—senior director of data, analytics and data science at The Clorox Company—to our Analytics Advantage podcast. A 17-year veteran of Clorox, Joshi leads a team of data management experts, advanced analytics professionals, and data scientists spread across business divisions and centers of excellence. These teams work on different analytics focus areas like business performance tracking and forecasting, advertising, trade promotions, market structures, and marketing personalization.
“The retail landscape is changing, and ecommerce is putting a tremendous amount of pressure on retailers, leading to greater emphasis on private label and that drives pressure on brand manufacturers like Clorox,” Joshi says. “Everyone is in a battle for consumer insights. So, we must get smarter and faster in generating those insights. We communicate this to our retailers make sure we keep the overall value front and center as we are building our products and creating our advertising. That’s where our analytics comes in.”
Like Clorox, consumer goods companies rely on a limited amount of available transactional data. Manufacturers might know where and when an item was sold and for how much, but nuanced data such as what else might have been in the shopping cart at the time of purchase could lead to deeper insight and brand loyalty.
“We have a lot of data about our consumer that can be used to create consumer clusters,” Joshi says. “We are not to a point where we are personalizing to a specific person but we are personalizing to groups of consumers. The number of groups of consumers that we talk to is growing.”
In the last three years, Joshi and his data science team have poured over the consumer segments with analytics tools to find out more about how these consumer clusters can be created in a way that can drive the effectiveness of our interactions with the consumers. This is similar to the age-old market segmentation, just order of magnitude more granular.
“We have done segmentation for so many years,” Joshi says. “This has resulted in five or six consumer clusters. With the amount of data that is available now, we have our data science team identifying many more different clusters and that can help us serve our consumers better.”
Joshi says another key to his team’s success is using analytics to see how dynamically the attributes are changing. “Analytics helps us identify those different consumer attributes that really matter,” Joshi says. “I could come up with 10,000 attributes, but I have to focus on the ones that are really important so we can supply customers with the right offers and communications.”
Often, CPG companies will use promotions to gauge how individual products are performing. These often use trade analytics that looks at trade promotion programs. By working with sales teams and retailers, the analytics helps companies determine how and where the promotions are working and potentially how to optimize them in the future.
“Pricing works off of fundamental measurement of the impact of our pricing and promotions. Joshi says. “Some of the pricing that we do is more tactical, but we are also involved with strategic pricing where we look at our three-year plans, we look at our products, we look at the markets, and we look at our competitive positions. From that we make recommendations and we look at what types of activities can be put in place to keep pricing low.”
Building the right data pipelines is critical to companies like Clorox for measuring and analyzing marketing optimization and effectiveness.
“You can look at specific brands and see how much they are spending on streaming videos versus banners versus television and optimizing the overall video spend,” Joshi says.
To hear more about Clorox and customer clustering, check out the entire conversation on the Oracle Analytics Podcast (click the icon below to listen on our Oracle podcast player):
Or listen on iTunes:
To find out more about how analytics can benefit your enterprise, visit our website and begin your journey into Oracle Analytics Cloud.
RosieReality, a Swiss startup using AR to get kids interested in robotics and programming, scores $2.2M seed
It’s no secret that business data of every conceivable type has grown exponentially, and many business analytics tools have sprung up to help organizations leverage this data productively. Unfortunately, many business people aren’t taking advantage yet. At the recent Gartner Data and Analytics event in Orlando, more than a few analysts repeated a factoid that was a bit surprising—only 35 percent of potential users of BI tools are in fact using them, leaving the other 65 percent without the insights they need, when they need them.
The reason for this gap becomes clear when you look at various business roles and their goals. Business people think in terms of outcomes—not data and tools—and the questions they need to answer are very specific:
- “How can I make my call center more efficient?”
- “How can I reduce the cost of new hires?”
- “Am I paying new recruits adequately?”
In the past, IT was tasked with providing the data and analyses these people needed to answer their questions, but the process took time and effort—often too much time and effort. Today, business analysts have taken the lead with self-service business intelligence (BI) tools for performing their own analyses. But, they still have to find the data, cleanse it, and mash it with other sources of data before they can even begin to answer questions they know about, never mind the ones they haven’t even thought of yet.
Everybody wants to be insight-driven, but what do they need to make that a reality?
Everyone claims that they want to be driven by insights fueled by rich data. How can that 65 percent be “enticed” to take advantage of the apps and tools that are available to help them analyze their data, reveal the secrets, and act on the results?
These three things make a difference. Business people need:
- Data that’s in proper context for their roles and responsibilities
- Information delivered where it will have optimal impact, sometimes within other applications
- Key performance indicators (KPIs) that not only identify where they’ve been, but also serve as early warning indicators of where they’re headed so that they can take corrective action, if necessary, to achieve their desired outcomes
The Analytics Wave Is Building Fast
At Oracle, we believe the answer lies in prebuilt analytics applications—the next big wave of innovation for analytics and business intelligence. These are role-based applications that deliver insights in the context of a business person’s role and they include both in-the-moment metrics as well as forward-looking KPIs. And we’re not the only ones who see this coming. According to Ventana Research, by 2021, more than half of all business analytics will come from prebuilt applications instead of just from business intelligence tools.
For some contexts, this is not a brand-new idea; analytics applications have been around for decades. I even spent a good chunk of my career building them, then evaluating them for clients.
However, over the past five to seven years, the emphasis in BI has been on self-service, with business analysts deriving useful content from any data they can get their hands on. This has given users a lot of freedom, but it has also created challenges, because business users can’t always be sure they are basing their decisions on the right data.
You Need Freedom Within a Framework
This problem is solved by analytics applications that combine traditional predefined analytics with the flexibility of self-service to deliver “freedom within a framework.” Here’s how it works.
- Role-based applications deliver modeled data. Business leaders receive sanctioned, vetted data directly from business applications so that everyone works from a single source of truth. Pre-delivered KPIs, metrics, and visualizations deliver insights that are meaningful to the user’s specific role. Executives, department heads, front-line managers, business managers—everybody can access information that’s relevant not only to their functional area, but also to their unique role.
- Business teams can tailor the process. They can create new KPIs and adjust existing ones, and they can visualize data to support new measures, attributes, and scenarios.
- Organizations can drive outcomes with analytics in context. Data from any source—core business applications in the cloud, on-premises sources, content from multiple clouds—can be combined in myriad ways using self-service BI to deliver more granular insights that allow management to drive business outcomes.
Whether you’re a chief financial officer or a hiring manager, this type of flexible framework lets you access the precise analytics you need to drive the best outcomes based on your role and responsibilities. One size definitely does not fit all; that’s why providing business people with role-based analytics applications that give them freedom of analysis within a solid, robust analytics framework can truly give them the best of both worlds.
To find out more about how analytics can benefit your enterprise, visit our website and begin your journey into Oracle Analytics Cloud.
Equity Shot: Pinterest zooms into the public markets (and yet another tech company files for an IPO)
Spotinst, the startup enabling companies to purchase and manage excess cloud capacity, acquires StratCloud
From the largest Fortune 100 companies to the most innovative startups, Chief Human Resources Officers (CHROs) play an integral role as transformative business leaders of their organizations. Although CHROs lead key back-office processes that support the bottom line, only 20 percent of HR professionals believe they can adequately plan for their company’s future talent needs, according to a study by Deloitte on organizational performance. Incidentally, respondents in another survey said that forecasting for headcount was one of the most important use cases of analytics in HR.
Imagine being able to make important business decisions based on robust analytics. If more HR teams had the ability to predict future headcount needs, recruiting departments could plan before an imminent hiring initiative, for example, and they could make relevant predictions for their organizations. Oracle Analytics Cloud is ideally positioned to help CHROs make more informed and timely HR decisions.
Low Unemployment Increases Turnover Risk
According to the Bureau of Labor Statistics, unemployment in the US as of December 2018 is 3.9 percent, the nation’s lowest in 19 years. With low unemployment, job candidates are in high demand. For a company conducting a hiring initiative, prospective candidates are harder to find, so recruiters tend to poach employees from competitors.
Similarly, websites like LinkedIn make it easy for employees to passively search for job opportunities and connect with other offers. Once a candidate sets LinkedIn job preferences to “open,” recruiters are notified. Scheduling an interview with a competitor is as simple as responding to a LinkedIn message from a recruiter.
Oracle Analytics Cloud can be used to gain insight into employee satisfaction and prevent turnover.
A friend of mine, employed as a senior electrical engineer at a medical device company, expressed interest in a management role within her department. However, because the company did not have an open management position, my friend started a passive job search while still employed and engaged on a project. She has had more than a few opportunities to interview at various companies in the San Francisco Bay Area. Thus, her company is now at risk of losing a senior-level engineer in the middle of a project. Lack of upward mobility is just one reason that an employee may not feel happy and fulfilled.
In a job market where companies are desperate to hire, but candidates have multiple opportunities lined up, what is keeping an experienced employee from leaving worth? To create an environment where employees are motivated to spend 40+ hours a week, CHROs have the responsibility to do everything they can to ensure that their employees feel fulfilled and are motivated to stay.
In a Candidate-Driven Job Market, Forecasting Helps Mitigate Turnover Risk
Determining the “how” in building employee fulfillment is an elusive task. Directly asking each employee or pulling insights from periodic performance reviews may be a starting point, but direct data collection via questionnaire is time-consuming, expensive, and may be subject to bias such as neutral responding.
Instead of a tedious collection process, CHROs can start by examining the data they already have that is associated with the employee lifecycle. Consider the process your business goes through each time an employee is hired, promoted, given a raise, conducts a performance review, or leaves the company. Each of these events has many datapoints associated with it. This type of information resides within your core HRIS, or even applicant tracking system. With Oracle Analytics Cloud, visualizing this data against other fields will help identify trends about your workforce, giving you the tools to forecast, make predictions, and take informed actions.
Attaining the right data to visualize the tenure of each member of your engineering team in terms of location, salary, the last time a raise was given, or even the number of vacation or sick days taken over time is possible with capabilities within Oracle Analytics Cloud. This information could show which engineers fit the demographic of those that have historically left your company. With this intelligence, predictive analysis becomes easier. If a top performing engineer starts taking more vacation days or sick days than normal, this could be a red flag. Predictive analysis could help you understand turnover rates and ultimately develop action plans regarding incentives such as promotions or raises. And, this may prevent the loss of a valuable employee.
Data Analytics in Practice
Extrapolating forward, analytics may be combined with other areas of a business to gain insights into the entire employee journey, including recruiting, onboarding, sourcing, performance, incentive compensation, or goal management. HR has the potential to strategically combine its insights with the organization’s overall business decision making process.
In a recruiting case study with one of the largest mobile providers in the US, the company took job applications of college hires and combined them with each respective employee’s performance reviews over time. Doing so allowed the company to discover that SAT scores and college grade-point averages are poor predictors of job candidate success. Better predictors of employee success proved to be experience that showed initiative and leadership, such as starting a club or leading a sports team. Staying ahead of the data analytics adoption curve, this company is seen as a leader in the industry.
In another case, at a global logistics company, data analytics has helped improve employees’ sense of fulfillment. The company employs thousands of delivery drivers. The drivers carry handheld devices that accept a signature to designate successful delivery, but those devices also leverage data analytics to provide the driver with an abundance of key information. The devices help solve the famous “travelling salesman problem”—or in this case, travelling delivery driver—allowing drivers to find the quickest path between multiple points and take the most efficient delivery route. Combined with Internet of Things (IoT) devices, these data-driven insights empower the company to increase the number of packages delivered per employee, thus optimizing driver productivity. When productivity per employee increases, the business is more profitable. With these improvements, the company compensates drivers at some of the highest rates in the industry, leading to overall happier employees, lower turnover, and higher retention.
Data tells important stories, and understanding those stories can positively enable employee fulfillment and the bottom line. Oracle Analytics Cloud provides the right technology and predictive capabilities so that you can make prescriptive determinations and take informed actions to improve your business.
In the Deloitte survey, only 19 percent of HR teams were able to derive suggested actions from data insights. To remain competitive in the candidate-driven job market, CHROs have the opportunity to leverage tools like Oracle Analytics Cloud to make better decisions about their people. The HR teams that possess a better view of their employee and candidate data will stake their claims as early adopters before a majority of their competitors do the same.
To learn more about using your data to make better decisions and complement your current strategy, visit the Oracle Analytics Cloud website.
Guest author, Ryan Pasca is an Applications Sales Representative specializing in Oracle Analytics.
Finding talented employees is only the beginning of the HR puzzle these days as organizations are adapting to rapid change, trying to improve recruiting and retention, and understanding better the business implications of these decisions.
As tech-savvy job seekers continue to enter the workforce, it is no wonder that many top-performing organizations are finding success by adopting an analytics strategy when it comes to human capital management.
Nevertheless, how can managers in human resources, finance, and general hiring offices avoid the “gut instinct” they have relied on until now and boldly move into a state where augmented analytics keeps the company running smoothly?
According to Deloitte Consulting LLP (Deloitte), during a Webcast titled Real-Time Talent Insights: Driving Next Generation Workforce Analytics, it is important to consider the nature of the work itself. ”There is a new set of age groups joining our workforce, and they are used to working on the internet. They have grown accustomed to working remotely. And they are creating opportunities for HR to use data analytics in their hiring and employment strategies.”
Based on a 2018 Oracle-sponsored study titled HR Moves Boldly into Advanced Analytics with the Help of Finance, several factors determine how well a human resources department can thrive and survive rapid change.
An HR issue holding many companies back is attrition or workforce turnover. Deloitte’s study of organizations with at least 2,000 employees in highly skilled positions noted that the total cost of losing an employee ranges from tens of thousands of dollars to as much as twice that employee’s annual salary. Mid-sized companies may find it even harder to replace key positions, spending up to $34 million annually. Deloitte estimates that companies can save as much as $7 million if they are able to retain even just 20 percent of employees who are looking to leave. Advanced analytics capabilities applied to areas like recruiting, turnover, job performance and location can go a long way towards helping companies retain quality workers.
“We are encouraging HR teams to use evidence-based decision making, which will have more positive outcomes,” Deloitte says.
Among the areas of opportunity for data analytics in HR, Deloitte notes six:
- Tools for engagement (including augmented analytics);
- Performance management;
- Cloud-based human resources management system (HRMS) “switch out”;
- Employee wellness;
- Reinventing recruitment;
- Introducing mobile apps (especially those that include natural language processing).
The rise in cloud-based, team-centric performance management systems that connect to HRMS or ERP systems is a great opportunity for business analytics platforms. The recent switch out of old HR software to cloud-based platforms is also an opportunity for companies to embed augmented analytics into their management systems.
“AI, robotics, and automation are all very big trends in the human resources area; and to be successful, you need that data,” Deloitte says. “Bringing together that people data and combining it with your ERP systems and your finance data will be very important to raising your company to the next level.”
Recruitment efforts are ripe for data analytics inclusion as companies shift to understanding their employee prospects instead of simply tracking applications. Mobile app use is also helping inspire leading companies to adopt data analytics in their HR processes.
Deloitte recommends “Daily tasks for employees that help them onboard faster or develop their skillsets.” Companies can monitor and measure these tasks with a simple mobile application. “Being able to put into place those toolsets for engagement is an important part of moving forward.”
Uber’s JUMP Bikes founder and Scoot’s SVP of Product are talking bikes and scooters at TC Sessions: Mobility
Because companies crave a competitive advantage and desire better insight into their data, more and more are adopting analytics and machine learning in the marketplace. Gartner predicts the business value created by artificial intelligence (AI) and machine learning will reach $3.9 trillion in 2022.
To put this into perspective, we invited Jeffrey Silverman, senior manager for Grant Thornton, to join us on the Oracle Analytics Advantage Podcast. As a leader in its Business Analytics practice, Silverman advises clients on the advantages of implementing cloud-based analytics embedded with machine learning to gain a competitive advantage.
“The industry is way beyond early adoption,” Silverman notes. “There are so many companies out there that see the value in cloud-based analytics that we are in the early majority. And when you are in that position, you’re going to reap the benefits and be ahead of your competition.”
Silverman is also a Lieutenant Colonel in the military reserves and is an intelligence director for the US Military where he leads the Big Data Initiative for US Strategic Command. He quips that there is such a compelling argument for using cloud-based analytics to derive insight from data that even the US government has adopted the strategy.
“If we’re already talking about cloud analytics, then a lot of people should be,” Silverman jokes.
Silverman divides the analytics world into four sections. There is descriptive (What is out there? What’s going on?); diagnostic (Why did this happen?); predictive (What will happen next?); and prescriptive (How do you make something happen?). He considers prescriptive the most sought after, yet most difficult to produce because it requires the most data and the most analysis.
“If you want to get beyond dashboards and reports and into predictive and prescriptive analytics, you absolutely need to adopt cloud-based and embedded machine learning analytics,” says Silverman. “But I will caution if you don’t have your data in order and you don’t have your descriptive or diagnostic analytics in order, and you try to feed this beast of machine learning…garbage in is garbage out.”
Getting to that blend of descriptive, diagnostic, predictive, and prescriptive analytics has great potential. An example of this is the practice of dynamic pricing.
“Hotel websites and other portals like VRBO need to adapt their pricing schedule to accommodate for demand,” says Silverman. “Low demand means the price is low. High demand means the price gets higher. Airlines do this too. Machine learning adapts these business rules and applies them to a new hotel or a new location or a new flight route—much faster than if you waited for an individual to do this manually.”
Taking a diagnostic approach can also reduce risk. If there is an emerging threat, for example, having cloud-based tools to mine unstructured content and unstructured data allows companies to react faster to customer concerns.
“If there is a choking hazard in a toy, wouldn’t it be great to troll through the various reviews that are on a product to see if maybe a customer posted it on their Amazon rating,” Silverman says. “If data discovery tools allow you to do that and go through something that is nonstandard data, it’s going to allow you to rapidly address an issue before it becomes truly destructive to your organization.”
The next phase is artificial intelligence and a layer of machine learning running over AI. You want to make decisions quickly and refine the roles that these layers play in a business analytics environment, allowing them to learn from their past mistakes. Whenever you want to be fast and potentially prescriptive, embedded machine learning will give you an advantage.
“One of the things we are looking at is how profitable is a particular product by historical trend,” Silverman says. “Then we take that trend and overlay it over a calendar and ask where we think we are going to be in the future based on consumption.”
Check out the entire conversation on the Oracle Analytics Podcast (click the icon below to listen):
And if you ever needed more convincing about the value of cloud-based analytics, the video below is our interview with Joseph Coniker, a principal at Grant Thornton, who explains how Oracle Analytics Cloud enabled Grant Thornton to integrate enterprise performance management and financial ERP data together to offer robust reporting analytics solutions for its customers.
Let’s face it—mobile devices are an indispensable part of our daily lives. These amazing devices keep us in touch with our friends and family, entertain us, and let us know exactly where we are and where we should be going. They enable our transport, be that by car, train, plane, or electric scooter. They monitor our daily activities and help us stay healthy. And they gently remind us when we stray too far from our target path. If we entrust them with the things that are most important to us in our personal lives, why not have them raise our professional lives to a new level of efficiency as well?
The pace of business is getting faster due to a number of factors, but the result is that we need to understand our current situation and make a decision to correct our course as fast as possible. Those organizations that can achieve this level of agility are set to beat their competition. However, if you’re spending more time away from your desk, either traveling, in meeting rooms, or moving through hallways, how do you stay focused on the most important things?
The Best Analytics You Have Are the Analytics You Have with You
Mobile analytics is not about opening some pre-built reports, whether designed for the micro form-factor or not. Attempting to emulate how we work on our laptops is a fool’s errand. How we interact with our mobile devices is fundamentally different. This is as true for our mobile personal lives as it is for our mobile professional lives.
What Our Professional Lives Should Expect from Mobile Analytics
Natural Language Querying That Is Voice-Enabled
We’ve become accustomed to using our voice to set reminders and alarms, create appointments, and choose what music we’d like to hear and when to skip tracks. This frees our hands from having to physically touch through an interface to enact our requests.
The same principle carries through to our business lives. Requesting information should be as natural as asking a colleague and doing that in your native language, at conventional speech pace and without having specific syntax to adhere to. Also, we can’t expect IT or anyone else to have predicted our always different business questions and pre-emptively created a report to satisfy our needs. Instead, no pre-authoring should be required, meaning responses are generated automatically on the fly with the assistance of smart technology.
See a demo of what I’m referring to at the link below. This example uses a request in French.
What you carry around with you every waking moment is a “smart device.” So how does that translate into our professional mobile needs?
To be smart, your mobile analytics should be capable of learning your routine, know where you go (GPS built in), what you look at regularly in which locations and at what times. That way, it can begin to proactively provide your content without you having to even ask for it in first place. As I regularly arrive at the office, my usual first task of the morning is to understand our current state of business. Why should I have to schedule that, or sit down and dig up some dashboard to tell me? My smart mobile should simply show me a notification, let me know my view has just been refreshed, here is our current state. If it’s all clear, great, time for coffee. But if something isn’t right, I can then jump into action right away. With this kind of real-time view, you can be connected to your business always. Thus, your professional mobile has become more than a method to render conventional dashboards or pie charts. It is now smart, suggesting new content without you having to ask. It remembers you, what you like to see and when. It becomes a real assistant as opposed to a static window into a conventional BI system.
See a location-aware demo here:
Our business lives, for most of us, are just as social as our personal lives. In our personal mobile lives, what we learn is shared as entertainment, or something learned provides relevant insight to our friends and family, so they can take action they otherwise may not have.
Similarly, our professional mobile lives rely on interactions between people. As your smart device proactively alerts you to something that is amiss, you need to be able to share that finding quickly with the relevant people to provoke action. Pace of business is increasing. Waiting until you’re at your desk, laptop running, dashboard open may be too late for some better decisions to have taken place. Instead, you must be able to take action as soon as you discover something. Sharing a chart, a grid of numbers, or a trend quickly and directly from your smart device is essential. As time progresses, your proactive mobile should also make connections between what’s interesting to you and to your colleagues. Your teammates may be slicing the information in a way you hadn’t considered. Those analytics are then recommended to you as alternate views. And likewise, your views are then recommended to your colleagues, in context.
Power of Mobile Transforms Business
Following the success factors that contributed to mobile adoption in our personal lives, your professional life can certainly benefit from mobile analytics if certain success criteria are replicated. Your professional mobile life demands natural language augmentation and the ability to be unrestricted in the questions you ask. It should become a true assistant, learn what is important to you, and be proactive. Finally, anything interesting must be quickly and easily sharable to speed the pace of business.
The following video shows how Oracle Analytics Day by Day specifically addresses these three requirements.
To see more of our mobile platform in action, visit the Oracle Analytics Cloud website.
We all know that data is the fuel that powers business growth today. Making the most of that data depends on getting it into the hands of business stakeholders so they can model, manipulate, and understand it better. The business analytics solution you choose will have a big impact on your organization’s success.
If you’re exploring data analytics solutions, you’ve got plenty of choices, including Oracle Analytics Cloud, Microsoft Power BI, and the desktop-based Tableau. But how can you tell which is best for enterprise-class insights? Over the last few months, I’ve had a chance to speak with industry experts about how these leading solutions compare to one another in terms of capabilities, scope, and usability. When you compare capabilities, experts agree that Oracle Analytics Cloud delivers the most complete, intuitive solution for scaling analytics to the enterprise.
Are You Really Seeing the Big Picture with Tableau?
Data visualization is a big part of unleashing the potential of data, but it’s only part of what’s required. To deliver a real payoff, you need business analytics that can go beyond just presenting data—you need business analytics that can go deeper into business processes themselves. According to Matt Yorke, Group Director at Qubix International, one of the main advantages of Oracle Analytics Cloud is its ability to not only deliver data visualization but provide a comprehensive view that spans the enterprise. Yorke says other solutions like Tableau have a much narrower scope, which can limit your ability to build meaningful analytics.
“Tableau made its mark by running department-level reports, and this is its Achilles’ heel,” Yorke says. “The problem is that each department then has its own—and different—view of the business. There is no single source of truth, and this compartmentalized view doesn’t scale to the enterprise.
“Oracle Analytics Cloud makes the flow of insights from end to end easy, without additional products. It can grab the data, orchestrate it, analyze it, report it—visually and in published reports,” Yorke says. “It can also do ‘what-if’ modeling. There’s no comparison with Tableau, which is very compartmentalized and limited.”
How Much Time Are You Spending Customizing Microsoft Power BI?
To be truly effective, your solution should unifiy around a single product, not force you to cobble together a patchwork of multiple tools. Competing solutions like Microsoft Power BI have plenty of potential, but they often require you to craft together your customized solution to start gaining the insights you need. According to Stewart Bryson, CEO and founder of Red Pill Analytics, Oracle Analytics Cloud delivers all these capabilities in a single solution that integrates seamlessly with Oracle Cloud services, so you can start using your solution—and seeing real benefits—faster.
“Most other providers can’t solve all the use cases,” Bryson says. “You have to buy from other providers, layer on solutions, and architect them to work together. Oracle Analytics Cloud gives you direct access to cloud dataset and cloud services. In comparison, Microsoft Power BI requires a lot of configuration and plumbing.”
Bryson also notes, “Oracle Analytics Cloud doesn’t have those requirements; you can just spin up a service. With Oracle Data Visualization, you can connect to all of your Oracle Cloud offerings, as well as to other tools, with minimal configuration.”
Getting More Value Faster
In a world where speed is everything, agility is key to an organization’s success. For enterprise organizations, Oracle’s rapid deployment and ease of use have delivered a substantial ROI. One global car rental company deployed Oracle Analytics Cloud in less than ten weeks, and implementation was smooth and successful. The company uses Oracle Analytics Cloud to display Oracle Business Intelligence dashboards and real-time financial data. These dashboards are used by operations and the corporation to monitor revenue and spending. Ninety-five percent of all these reports run in less than 60 seconds. Ultra-fast reporting means improved decision-making and rapid response to change.
Oracle Analytics Cloud Data Visualization is the new interface used by the company’s analysts and data scientists for data preparation, data model creation, machine learning, and more. Analysts can now connect to multiple data sources, including Oracle Essbase cubes, and they can also import their own data in one click.
Don’t Waste Any More Time with Incomplete Analytics Tools
With so many analytics solutions available, you can easily spend a lot of cycles building and fine-tuning a customized, multitool analytics solution. But is that what you want to be doing with your time? If so, be prepared for hidden integration and configuration costs, along with inconsistent insight into your business. Or you can eliminate a lot of headaches and all of that lost time, and simply choose Oracle Analytics Cloud.
If you want to see how our industry experts came to their conclusions, we’ve set up a website that outlines the difference between Oracle Analytics Cloud and the competition. We also encourage you to read our Technical Buyers Guide to Analytics, which includes more detailed analysis.