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Microsoft has been steadily pouring money into big data and business intelligence. The company of course owns the most widely used analytical tool in the world, Microsoft Excel, which our benchmark research into Spreadsheets in the Enterprise shows is not going away soon. User resistance (cited by 56% of participants) and lack of a business case (50%) are the most common reasons that spreadsheets are not being replaced in the enterprise. The challenge is ensuring the spreadsheets are not just personally used but connected and secured into the enterprise for a range of consistency and potential errors that all add up to more work and maintenance as my colleague has pointed out recently.
Along with Microsoft SQL and SharePoint, Excel is at the heart of the company’s BI strategy. In particular, PowerPivot, originally introduced as an add-on for Excel 2010 and built into Excel 2013, is a discovery tool that enables exploratory analytics and data mashups. PowerPivot uses an in-memory, column store approach similar to other tools in the market. Its ability to access multiple data sources including from third parties and government through Microsoft’s Azure Marketplace, enables a robust analytical experience.
Ultimately, information sources are more important than the tool sets used on them. With the Azure Marketplace and access to other new data sources such as Hadoop through partnership with Hortonworks as my colleague assessed, Microsoft is advancing in the big data space. Microsoft has partnered with Hortonworks to bring Hadoop data into the fold through HDInsights, which enable familiar Excel environments to access HDFS via HCatalog. This approach is similar to access methods utilized by other companies, including Teradata which I wrote about last week. Microsoft stresses the 100 percent open source nature of the Hortonworks approach as a standard alternative to the multiple, more proprietary Hadoop distributions occurring throughout the industry. An important benefit for enterprises with Microsoft deployments is that Microsoft Active Directory adds security to HDInsights.
As my colleague Mark Smith recently pointed out about data discovery methods, the analytic discovery category is broad and includes visualization approaches. On the visualization side, Microsoft markets PowerView, also part of Excel 2013, which provides visual analytics and navigation on top of the Microsoft’s BI semantic model. Users also can annotate and highlight content and then embed it directly into PowerPoint presentations. This direct export feature is valuable because PowerPoint is still a critical communication vehicle in many organizations. Another visual tool, currently in preview, is the Excel add-in GeoFlow, which uses Bing Maps to render visually impressive temporal and geographic data in three dimensions. Such a 3-D visualization technique could be useful in many industries. Our research into next generation business intelligence found that deploying geographic maps (47%) and visualizing metrics on them (41%) are becoming increasing important but Microsoft will need to further exploit location-based analytics and the need for interactivity.
Microsoft has a core advantage in being able to link its front-office tools such as Excel with its back-end systems such as SQL Server 2012 and SharePoint. In particular, having the ability to leverage a common semantic model through Microsoft Analytical Services, in what Microsoft calls its Business Intelligence Semantic Model, users can set up a dynamic exploratory environment through Excel. Once users or analysts have developed a BI work product, they can publish the work product such as a report directly or through SharePoint. This integration enables business users to share data models and solutions and manage them in common, which applies to security controls as well as giving visibility into usage statistics to see when particular applications are gaining traction with organizational users.
Usability, which our benchmark research into next-generation business intelligence
identifies as the number-one evaluation criterion in nearly two-thirds (64%) of organizations, is still a challenge for Microsoft. Excel power users will appreciate the solid capabilities of PowerPivot, but more casual users of Excel – the majority of business people – do not understand how to build pivot tables or formulas. Our research shows that only 11 percent of Excel users are power users and most skill levels are simply adequate (49%) compared to above average or excellent. While PowerView does give some added capability, a number of other vendors of visual discovery products like Tableau have focused on user experience from the ground up, so it is clear that Microsoft needs to address this shortcoming in its design environment.
When we consider more advanced analytic strategies and inclusion of advanced algorithms, Microsoft’s direction is not clear. Its Data Analysis eXpressions (DAX) can help create custom measures and calculated fields, but it is a scripting language akin to MDX. This is useful for IT professionals who are familiar with such tools, but here also business-oriented users will be challenged in using it effectively.
A wild card in Microsoft’s BI and analytics strategy is with mobile technology. Currently, Microsoft is pursuing a build-once, deploy-anywhere model based on HTML5, and is a key member of the Worldwide Web Consortium (W3C) that is defining the standard. The HTML5 standard, which has just passed a big hurdle in terms of candidate recommendation is beginning to show value in the design of new applications that can be access through web-browsers on smartphones and tablets. However, the success or failure of its Windows 8-based Surface tablet will be the real barometer since its integration with the Office franchise is a key differentiator. This approach of HTML5 could be challenging as our technology innovation research into mobile technology finds more organizations (39%) prefer native mobile applications from the vendors specific application stores compared to 33 percent through web-browser based method and a fifth with no preference. Early adoption of the tablet has not been strong, but Microsoft is said to be doubling down with a new version to be announced shortly. Success would put Office into the hands of the mobile workforce on a widespread basis via Microsoft devices, which could have far-reaching impacts for the mobile BI market.
As it stands now, however, Microsoft faces an uphill battle in establishing its mobile platform in a market dominated by Android and Apple iOS devices like the iPhone and iPad. If the Surface ultimately fails, Microsoft will likely have to open up Office to run on Android and iOS or risk losing its dominant position. My colleague is quite pessimistic about Microsoft overall mobile technology efforts and its ability to overcome the reality of the existing market. Our technology innovation research into mobile technology finds that over half of organizations have a preference for their smartphone and tablet technology platform, and the first ranked smartphone priorities has Apple (50%), Android (27%) and RIM (17%) as top smartphone platforms with Microsoft a distant fourth (5%); for tablets is Apple (66%), Android (19%) and then Microsoft (8%). Based on these finding, Microsoft faces challenges on both the platform front and if they adapt their technology to support others that are more preferred and used in business today.
Ultimately, Microsoft is trying to pull together different initiatives across multiple internal business units that are known for being very siloed and not organized well for customers. Ultimately, Microsoft has relied on its channel partners and customers to figure out how to not just make them work together but also think about what is possible since they are not always given clear guidance from Redmond. Recent efforts find that Microsoft is trying to come together to address the big data and business analytics challenge and the massive opportunity it represents. One area in which this is coming together is Microsoft’s cloud initiatives. Last year’s announcements of Azure virtual machines enables an infrastructure-as-a-service (IaaS) play for Microsoft and positions Windows Azure SQL Database as a service. This could make the back end systems I’ve discussed available through a cloud-based. Ironically, the cloud-based Office365 suite does not include core productivity applications such as Excel and PowerPoint, so front-end access will still come through the client version of the software.
For organizations that already have installed Microsoft as their primary BI platform and are looking for tight integration with an Excel-based discovery environment, the decision to move forward is relatively simple. The trade-off is that this package is still a bit IT-centric and may not attract as many in the larger body of business users as a more user-friendly discovery product might do and address the failings of business intelligence. Furthermore, since Microsoft is not as engaged in direct support and service as other players in this market, it will need to move the traditionally technology focused channel to help their customers become more business savvy. For marketing and other business departments, especially in high-velocity industries where usability and time-to-value is at a premium and back-end integration is secondary, other tools will be worth a look. Microsoft has great potential and with analytics being the top ranked technology innovation priority among its customers I hope that the many divisions inside the global software giant can finally come together to deliver a comprehensive approach.
Regards,
Tony Cosentino
VP and Research Director
Our benchmark research found in business technology innovation that analytics is the most important new technology for improving their organization’s performance; they ranked big data only fifth out of six choices. This and other findings indicate that the best way for big data to contribute value to today’s organizations is to be paired with analytics. Recently, I wrote about what I call the four pillars of big data analytics on which the technology must be built. These areas are the foundation of big data and information optimization, predictive analytics, right-time analytics and the discovery and visualization of analytics. These components gave me a framework for looking at Teradata’s approach to big data analytics during the company’s analyst conference last week in La Jolla, Calif.
The essence of big data is to handle the variety of data but also optimize the information used by the business for whatever type of needs as my colleague has identified as a key value of these investments. 
Data diversity presents a challenge to most enterprise data warehouse architectures. Teradata has been dealing with large, complex sets of data for years, but today’s different data types are forcing new modes of processing in enterprise data warehouses Teradata is addressing this issue by focusing on a workload-specific architecture that aligns with MapReduce, statistics and SQL. Its Unified Data Architecture (UDA) incorporates the Hortonworks Hadoop distribution, the Aster Data platform and Teradata’s stalwart RDBMS EDW. The Big Data Analytics appliance won our annual innovation award in 2012. The system is connected through Infiniband and accesses Hadoop’s metadata layer directly through Hcatalog. Bringing these pieces together represents the type of holistic thinking that is critical for handling big data analytics; at the same time there are some costs as the system includes two MapReduce processing environments. For more on the UDA architecture, read my previous post on Teradata as well as my colleague Mark Smith’s piece.
Predictive analytics is another foundational piece of big data analytics and one of the top priorities in organizations according to our 
big data research is not available in 41 percent of organizations today. Teradata is addressing it in a number of ways and at the conference Stephen Brobst, Teradata’s CTO, likened big data analytics to a high-school chemistry classroom that has a chemical closet from which you pull out the chemicals needed to perform an experiment in a separate work area. In this analogy, Hadoop and the RDBMS EDW are the chemical closet, and Aster Data provides the sandbox where the experiment is conducted. With mulitple algorithms currently written into the platform and many more promised over the coming months, this sandbox provides a promising big data lab environment. The approach is SQL-centric and as such has its pros and cons. The obvious advantage is that SQL is a declarative language that is easier to learn than procedural languages, and an established skills base exists within most organizations. The disadvantage is that SQL is not the native tongue of many business analysts and statisticians. While it may be easy to call a function within the context of the SQL statement, the same person who can write the statement may not know when and where to call the function. One way for Teradata to expediently address this need is through its existing partnerships with companies like Alteryx, which I wrote about recently. Alteryx provides a user-friendly analytical workflow environment and is establishing a solid presence on the business side of the house. Teradata already works with predictive analytics providers like SAS but should further expand with companies like Revolution Analytics that I assessed that are using R technology to support a new generation of tools.
Teradata is exploiting its advantage with algorithms such as nPath, which shows the path that a customer has taken to a particular outcome such as buying or not buying. According to our big data benchmark research, being able to conduct what-if analysis and predictive analytics are the two most desired capabilities not currently available with big data, as the chart shows. The algorithms that Teradata is building into Aster help address this challenge, but despite customer case studies shown at the conference, Teradata did not clearly demonstrate how this type of algorithm and others seamlessly integrate to address the overall customer experience or other business challenges. While presenters verbalized it in terms of improving churn and fraud models, and we can imagine how the handoffs might occur, the presentations were more technical in nature. As Teradata gains traction with these types of analytical approaches, it will behoove the company to show not just how the algorithm and SQL works but how it works in the use by business and analysts who are not as technical savvy.
Another key principle behind big data analytics is timeliness of the analytics. Given the nature of business intelligence and traditional EDW architectures, until now timeliness of analytics has been associated with how quickly queries run. This has been a strength of the Teradata MPP share-nothing architecture, but other appliance architectures, such as those of Netezza and Greenplum, now challenge Teradata’s hegemony in this area. Furthermore, trends in big data make the situation more complex. In particular, with very large data sets, many analytical environments have replaced the traditional row-level access with column access. Column access is a more natural way for data to be accessed for analytics since it does not have to read through an entire row of data that may not be relevant to the task at hand. At the same time, column-level access has downsides, such as the reduced speed at which you can write to the system; also, as the data set used in the analysis expands to a high number of columns, it can become less efficient than row-level access. Teradata addresses this challenge by providing both row and column access through innovative proprietary access and computation techniques.
Exploratory analytics on large, diverse data sets also has a timeliness imperative. Hadoop promises the ability to conduct iterative analysis on such data sets, which is the reason that companies store big data in the first place according to our big data benchmark research. Iterative analysis is akin to the way the human brain naturally functions, as one question naturally leads to another question. However, methods such as Hive, which allows an SQL-like method to access Hadoop data, can be very slow, sometimes taking hours to return a query. Aster enables much faster access and therefore provides a more dynamic interface for iterative analytics on big data.
Timeliness also has to do with incorporating big data in a stream-oriented environment and only 16 percent of organizations are very satisfied with timeliness of events according to our operational intelligence benchmark research. In a use case such as fraud and security, rule-based systems work with complex algorithmic functions to uncover criminal activity. While Teradata itself does not provide the streaming or complex event processing (CEP) engines, it can provide the big data analytical sandbox and algorithmic firepower necessary to supply the appropriate algorithms for these systems. Teradata partner with major players in this space already, but would be well served to further partner with CEP and other operational intelligence vendors to expand its footprint. By the way, these vendors will be covered in our upcoming Operational Intelligence Value Index, which is based on our operational intelligence benchmark research that found it important to analyze business and IT events that was found to be very important in 45 percent of organizations.
The visualization and discovery of analytics is the last foundation 
and here Teradata is still a work in progress. While some of the big data visualizations Aster generates show interesting charts, they lack a context to help people interpret the chart. Furthermore, the visualization is not as intuitive and requires the writing and customization of SQL statements. To be fair, most visual and discovery tools today are relationally oriented and Teradata is trying to visualize large and diverse sets of data. Furthermore, Teradata partners with companies including MicroStrategy and Tableau to provide more user-friendly interfaces. As Teradata pursues the big data analytics market, it will be important to demonstrate how it works with its partners to build a more robust and intuitive analytics workflow environment and visualization capability for the line-of-business user. Usability (63%) and functionality (49%) are the top two considerations when evaluating business intelligence systems according to our research on next-generation business intelligence.
Like other large industry technology players, Teradata is adjusting to the changes brought by business technology innovation in just the last few years. Given its highly scalable databases and data modeling – areas that still represent the heart of most company’s information architectures – Teradata has the potential to pull everything all together and leverage their current deployed base. Technologists looking at Teradata’s new and evolving capabilities will need to understand the business use cases and share these with the people in charge of such initiatives. For business users, it is important to realize that big data is more than just visualizing disparate data sets and that greater value lies in setting up an efficient back end process that applies the right architecture and tools to the right business problem.
Regards,
Tony Cosentino
VP and Research Director
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