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Users of big data analytics are finally going public. At the Hadoop Summit last June, many vendors were still speaking of a large retailer or a big bank as users but could not publically disclose their partnerships. Companies experimenting with big data analytics felt that their proof of concept was so innovative that once it moved into production, it would yield a competitive advantage to the early mover. Now many companies are speaking openly about what they have been up to in their business laboratories. I look forward to attending the 2013 Hadoop Summit in San Jose to see how much things have changed in just a single year for Hadoop centered big data analytics.
Our benchmark research into operational intelligence, which I argue is another name for real-time big data analytics, shows diversity in big data analytics use cases by industry. The goals of operational intelligence are an interesting mix as the research shows relative parity among managing performance (59%), detecting fraud and security (59%), complying with regulations (58%) and managing risk (58%), but when we drill down into different industries there are some interesting nuances. For instance, healthcare and banking are driven much more by risk and regulatory compliance, services such as retail are driven more by performance, and manufacturing is driven more by cost reduction. All of these make sense given the nature of the businesses. Let’s look at them in more detail.
The retail industry, driven by market forces and facing discontinuous change, is adopting big data analytics out of competitive necessity. The discontinuity comes in the form of online shopping and the need for traditional retailers to supplement their brick-and-mortar locations. JCPenney and Macy’s provide a sharp contrast in how two retailers approached this challenge. A few years ago, the two companies eyed a similar competitive space, but since that time, Macy’s has implemented systems based on big data analytics and is now sourcing locally for online transactions and can optimize pricing of its more than 70 million SKUs in just one hour using SAS High Performance Analytics. The Macy’s approach has, in Sun-Tzu like fashion, made the “showroom floor” disadvantage into a customer experience advantage. JCPenney, on the other hand, used gut-feel management decisions based on classic brand merchandising strategies and ended up alienating its customers and generating law suits and a well-publicized apology to its customers. Other companies including Sears are doing similarly innovative work with suppliers such as Teradata and innovative startups like Datameer in data hub architectures build around Hadoop.
Healthcare is another interesting market for big data, but the dynamics that drive it are less about market forces and more about government intervention and compliance issues. Laws around HIPPA, the recent Healthcare Affordability Act, OC-10 and the HITECH Act of 2009 all have implications for how these organizations implement technology and analytics. Our recent benchmark research on governance, risk and compliance indicates that many companies have significant concerns about compliance issues: 53 percent of participants said they are concerned about them, and 42 percent said they are very concerned. Electronic health records (EHRs) are moving them to more patient-centric systems, and one goal of the Affordable Care Act is to use technology to produce better outcomes through what it calls meaningful use standards. Facing this title wave of change, companies including IBM analyze historical patterns and link it with real-time monitoring, helping hospitals save the lives of at-risk babies. This use case was made into a now-famous commercial by advertising firm Ogilvy about the so-called data babies. IBM has also shown how cognitive question-and-answer systems such as Watson assist doctors in diagnosis and treatment of patients.
Data blending, the ability to mash together different data sources without having to manipulate the underlying data models, is another analytical technique gaining significant traction. Kaiser Permanente is able to use tools from Alteryx, which I have assessed, to consolidate diverse data sources, including unstructured data, to streamline operations to improve customer service. The two organizations made a joint presentation similar to the one here at Alteryx’s user conference in March.
Financial services, which my colleague Robert Kugel covers, is being driven by a combination of regulatory forces and competitive market forces on the sales end. Regulations produce a lag in the adoption of certain big data technologies, such as cloud computing, but areas such as fraud and risk management are being revolutionized by the ability, provided through in-memory systems, to look at every transaction rather than only a sampling of transactions through traditional audit processes. Furthermore, the ability to pair advanced analytical algorithms with in-memory real-time rules engines helps detect fraud as it occurs, and thus criminal activity may be stopped at the point of transaction. On a broader scale, new risk management frameworks are becoming the strategic and operational backbone for decision-making in financial services.
On the retail banking side, copious amounts of historical customer data from multiple banking channels combined with government data and social media data are providing banks the opportunity to do microsegmentation and create unprecedented customer intimacy. Big data approaches to micro-targetting and pricing algorithms, which Rob recently discussed in his blog on Nomis, enable banks and retailers alike to target individuals and customize pricing based on an individual’s propensity to act. While partnerships in the financial services arena are still held close to the vest, the universal financial services providers – Bank of America, Citigroup, JPMorgan Chase and Wells Fargo – are making considerable investments into all of the above-mentioned areas of big data analytics.
Industries other than retail, healthcare and banking are also seeing tangible value in big data analytics. Governments are using it to provide proactive monitoring and responses to catastrophic events. Product and design companies are leveraging big data analytics for everything from advertising attribution to crowdsourcing of new product innovation. Manufacturers are preventing downtime by studying interactions within systems and predicting machine failures before they occur. Airlines are recalibrating their flight routing systems in real time to avoid bad weather. From hospitality to telecommunications to entertainment and gaming, companies are publicizing their big data-related success stories.
Our research shows that until now, big data analytics has primarily been the domain of larger, digitally advanced enterprises. However, as use cases make their way through business and their tangible value is accepted, I anticipate that the activity around big data analytics will increase with companies that reside in the small and midsize business market. At this point, just about any company that is not considering how big data analytics may impact its business faces an unknown and uneasy future. What a difference a year makes, indeed.
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 optimize the information used by the business for whatever type of need 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 that encompasses the UDA framework 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. However, according to our big data research, it 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 technically 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 partners 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. This same research showed that analyzing business and IT events together was very important in 45 percent of organizations.
The visualization and discovery of analytics is the last foundational pillar 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 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
This year’s Inspire, Alteryx’s annual user conference, featured new developments around the company’s analytics platform. Alteryx CEO Dean Stoecker kicked off the event by talking about the promise of big data, the dissemination of analytics throughout the organization, and the data artisan as the “new boss.” Alteryx coined the term “data artisan” to represent the persona at the center of the company’s development and marketing efforts. My colleague Mark Smith wrote about the rise of the data artisan in his analysis of last year’s event.
President and COO George Mathew keynoted day two, getting into more specifics on the upcoming 8.5 product release. Advancements revolve around improvement in the analytical design environment, embedded search capabilities, the addition of interactive mapping and direct model output into Tableau. The goal is to provide an easier, more intuitive user experience. Our benchmark research into next-generation business intelligence shows buyers consider usability the top buying criteria at 63 percent. The redesigned Alteryx interface boasts a new look for the icons and more standardization across different functional environments. Color coding of the toolbox groups tools according to functions, such as data preparation, analytics and reporting. A new favorites function is another good addition, given that users tend to rely on the same tools depending on their role within the analytics value chain. Users can now look at workflows horizontally and not just vertically, and easily change the orientation if for example they are working on an Apple iPad. Version 8.5 allows embedded search and more streamlined navigation, and continues its focus on a role-based application, which my colleague has been advocating for a while. According to the company, 94 percent of its user base demanded interactive mapping; that’s now part of the product, letting users draw a polygon around an area of interest, then integrate it into the analytical application for runtime execution.
The highlight of the talk was the announcement of integration with Tableau 8.0 and the ability to write directly to the software without having to follow the cumbersome process of exporting a file and then reopening it in another application. Alteryx was an alpha partner and worked directly with the code base for Tableau 8.0, which I wrote up a few months ago. The partnership exemplifies the coopetition environment that many companies find themselves in today. While Tableau does some basic prediction, and Alteryx does some basic visual reporting, the companies’ core competencies brought together into one workflow is much more powerful for the user. Another interesting aspect is the juxtaposition of the two user groups. The visually oriented Tableau group in San Diego seemed much younger and was certainly much louder on the reveals, while the analytically oriented Alteryx group was much more subdued.
Alteryx has been around since 1997, when it was called SRC. It grew up focused around location analytics, which allowed it to establish foundational analytic use cases in vertical areas such as real estate and retail. After changing the company name and focusing more on horizontal analytics, Alteryx is growing fast with backing from, interestingly enough, SAP Ventures. Since the company was already profitable, it used a modest infusion of capital to grow its product marketing and sales functions. The move seems to have paid off. Companies such as Dunkin Brands and Redbox use Alteryx and the company has made significant inroads with marketing services companies. A number of consulting companies, such as Absolute Data and Capgemini, are using Alteryx for customer and marketing analytics and other use cases. I had an interesting talk with the CEO of a small but important services firm who said that he is being asked to introduce innovative analytical approaches to much larger marketing services and market research firms. He told me that Alteryx is a key part of the solution he’ll be introducing to enable things such as big data analytics.
Alteryx provides value in a few innovative ways that are not new to this release, but that are foundational to the company’s business strategy. First, it marries data integration with analytics, which allows business users who have traditionally worked in a flat-file environment to pull from multiple data sources and integrate information within the context of the Alteryx application. Within that same environment, users can build analytic workflows and publish applications to a private or public cloud. This approach helps address the obstacles found in our research in big data analytics where staffing (79%) and training (77%) are addressed by Alteryx through providing more flexibility for business to engage into the analytic process.
Alteryx manages an analytics application store called the Analytics Gallery that crowdsources and shares user-created models. These analytical assets can be used internally within an organization or sold on the broader market. Proprietary algorithms can be secured through a black box approach, or made open to allow other users to tweak the analytic code. It’s similar to what companies like Datameer are doing on top of Hadoop, or Informatica in the cloud integration market. The store gives descriptions of what the applications do, such as fuzzy matching or target marketing. Being crowdsourced, the number of applications should proliferate over time, tracking advancements in the R open source project, since R is at the heart of the Alteryx analytic strategy and what it calls clear box analytics. The underlying algorithm is easily viewed and edited based on permissions established by the data artisan, similar to what we’ve seen with companies such as 1010data. Alteryx 8.5 works with R 3.0, the latest version. On the back end, Alteryx partners with enterprise data warehouse powerhouses such as Teradata, and works with the Hortonworks Hadoop distribution.
I encourage analysts of all stripes to take a look at the Alteryx portfolio. Perhaps start with the Analytics Gallery to get a flavor of what the company does and the type of analytics customers are building and using today. Alteryx can benefit analysts looking to move beyond the limitations of a flat-file analytics environment, and especially marketing analysts who want to marry third-party data from sources such as the US Census Bureau, Experian, TomTom or Salesforce, which Alteryx offers within its product. If you have not seen Alteryx, you should take a look and see how they are changing the way analytic processes are designed and managed.
Regards,
Tony Cosentino
VP and Research Director