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Big data has become a big deal as the technology industry has invested tens of billions of dollars to create the next generation of databases and data processing. After the accompanying flood of new categories and marketing terminology from vendors, most in the IT community are now beginning to understand the potential of big data. Ventana Research thoroughly covered the evolving state of the big data and information optimization sector in 2014 and will continue this research in 2015 and beyond. As it progresses the importance of making big data systems interoperate with existing enterprise and information architecture along with digital transformation strategies becomes critical. Done properly companies can take advantage of big data innovations to optimize their established business processes and execute new business strategies. But just deploying big data and applying analytics to understand it is just the beginning. Innovative organizations must go beyond the usual exploratory and root-cause analyses through applied analytic discovery and other techniques. This of course requires them to develop competencies in information management for big data.
Among big data technologies, the open source Hadoop has been commercialized by now established providers including Cloudera, Hortonworks and MapR and made available in the cloud through platforms such as Qubole, which received a Ventana Research Technology Innovation Award in 2014. Other big data technologies are growing as well; for example, use of in-memory and specialized databases also is growing like Hadoop in more than 40 percent of organizations, according to our big data integration benchmark research. These technologies have been integrated into databases or what I call hybrid big data appliances like those from IBM, Oracle, SAP and Teradata that bring the power of Hadoop to the RDBMS and exploit in-memory processing to perform ever faster computing. When placed into hosted and cloud environments these appliances can virtualize big data processing. Another new provider, Splice Machine, brings the power of SQL processing in a scalable approach that uses Hadoop in a cloud-based approach; it received a Ventana Research Technology Leadership Award last year. Likewise advances in NoSQL approaches help organizations process and utilize semistructured information along with other information and blend them with analytics as Datawatch does. These examples show that disruptive technologies still have the potential to revolutionize our approaches to managing information.
Our firm also explores what we call information optimization, which assesses techniques for gaining full value from business information. Big data is one of these when used effectively in an enterprise information architecture. In this context the “data lake” analogy is not helpful in representing the full scope of big data, suggesting simply a container like a data marts or data warehouse. With big data, taking an architectural approach is critical. This viewpoint is evident in our 2014 Ventana Research Technology Innovation Award in Information Management to Teradata for its Unified Data Architecture. Another award winner, Software AG, blends big data and information optimization using its real-time and in-memory processing technologies.
Businesses need to process data in rapid cycles, many in real time and what we call operational intelligence, which utilizes events and streams and provides the ability to sense and respond immediately to issues and opportunities in organizations that adapt to a data-driven culture. Our operational intelligence research finds that monitoring, alerting and notification are the top use cases for deployment, in more than half of organizations. Also machine data can help businesses optimize not just IT processes but business processes that help govern and control the security of data in the enterprise. This imperative is evident in the dramatic growth of suppliers such as Splunk, Sumo Logic and Savi Technology, all of which won Ventana Research Technology Innovation awards for how they process machine and business data in large volumes at rapid velocity.
Another increasing trend in big data is presenting it in ways that ordinary users can understand quickly. Discovery and advanced visualization is not enough for business users who are not trained to interpret these presentations. Some vendors can present location and geospatial data on maps that are easier to understand. At the other end of the user spectrum data scientists and analysts need more robust analytic and discovery tools, including predictive analytics, which is a priority for many organizations, according to our big data analytics research. In 2015 we will examine the next generation of predictive analytics in new benchmark research. But there is more work to do to present insights from information that are easy to understand. Some analytics vendors are telling stories by linking pages of content, but these narratives don’t as yet help individuals assess and act. Most analytics tools can’t match the simple functionality of Microsoft PowerPoint, placing descriptive titles, bullets and recommendations on a page with a graphic that represents something important to these business professional who reads it. Deeper insights may come from advances in machine learning and cognitive computing that have arrived on the market and bring more science to analytics.
So we strong potential for the outputs of big data, but they don’t arrive just by loading data into these new computing environments. Pragmatic and experienced professionals realize that information management processes do not disappear. A key one in this area is data preparation, which helps ready data sets for processing into big data environments. Preparing data is the second-most important task for 46 percent of organizations in our big data integration research. A second is data integration, which some new tools can automate. This can enable lines of business and IT to work together on big data integration, as 41 percent of organizations in our research are planning to do. To address this need a new generation of technologies came into their own in 2014 including those that received Ventana Research Technology Innovation Awards like Paxata and Tamr but also Trifacta.
Yet another area to watch is the convergence of big data and cloud computing. The proliferation of data sources in the cloud forces organizations to managed and integrate data from a variety of cloud and Internet sources, hence the rise of information as a service for business needs. Ventana Research Technology Innovation Award winner DataSift provides information as a service to blend social media data with other big data and analytics. Such techniques require more flexible environments for integration that can operate anywhere at any time. Dell Boomi, MuleSoft, SnapLogic and others now challenge established data integration providers such as Informatica and others including IBM, Oracle and SAP. Advances in master data management, data governance, data quality and integration backbones, and Informatica and Information Builders help provide better consistency of any type of big data for any business purpose. In addition our research finds that data security is critical for big data in 61 percent of organizations; only 14 percent said that is very adequate in their organization.
There is no doubt that big data is now widespread; almost 80 percent of organizations in our information optimization research, for example, will be using it some form by the end of 2015. This is partly due to increased use across the lines of business; our research on next-generation customer analytics in 2014 shows that it is important to improving understanding customers in 60 percent of organizations, is being used in one-fifth of organizations and will be in 46 percent by the end of this year. Similarly our next-generation finance analytics research in 2014 finds big data important to 37 percent of organizations, with 13 percent using it today and 42 percent planning to by the end of 2015. And we have already measured how it will impact human capital management and HR and where organizations are leveraging it in this area of importance.
I invite you to download and peruse our big data agenda for 2015. We will examine how organizations can instrument information optimization processes that use big data and pass this guidance along. We will explore big data’s role in sales and product areas and produce new research on data and analytics in the cloud. Our research will uncover best practices that innovative organizations use not only to prepare and integrate big data but also more tightly unify it with analytics and operations across enterprise and cloud computing environments. For many organizations taking on this challenge and seeking its benefits will require new information platforms and methods to access and provide information as part of their big data deployments. (Getting consistent information across the enterprise is the top benefit of big data integration according to 39 percent of organizations.) We expect 2015 to be a big year for big data and information optimization. I look forward to providing more insights and information about big data and helping everyone get the most from their time and investments in it.
CEO and Chief Research Officer
A few months ago, I wrote an article on the four pillars of big data analytics. One of those pillars is what is called discovery analytics or where visual analytics and data discovery combine together to meet the business and analyst needs. My colleague Mark Smith subsequently clarified the four types of discovery analytics: visual discovery, data discovery, information discovery and event discovery. Now I want to follow up with a discussion of three trends that our research has uncovered in this space. (To reference how I’m using these four discovery terms, please refer to Mark’s post.)
The most prominent of these trends is that conversations about visual discovery are beginning to include data discovery, and vendors are developing and delivering such tool sets today. It is well-known that while big data profiling and the ability to visualize data give us a broader capacity for understanding, there are limitations that can be addressed only through data mining and techniques such as clustering and anomaly detection. Such approaches are needed to overcome statistical interpretation challenges such as Simpson’s paradox. In this context, we see a number of tools with different architectural approaches tackling this obstacle. For example, Information Builders, Datameer, BIRT Analytics and IBM’s new SPSS Analytic Catalyst tool all incorporate user-driven data mining directly with visual analysis. That is, they combine data mining technology with visual discovery for enhanced capability and more usability. Our research on predictive analytics shows that integrating predictive analytics into the existing architecture is the most pressing challenge (for 55% or organizations). Integrating data mining directly into the visual discovery process is one way to overcome this challenge.
The second trend is renewed focus on information discovery (i.e., search), especially among large enterprises with widely distributed systems as well as the big data vendors serving this market. IBM acquired Vivisimo and is incorporating the technology into its PureSystems and big data platform. Microsoft recently previewed its big data information discovery tool, Data Explorer. Oracle acquired Endeca and has made it a key component of its big data strategy. SAP added search to its latest Lumira platform. LucidWorks, an independent information discovery vendor that provides enterprise support for open source Lucene/Solr, adds search as an API and has received significant adoption. There are different levels of search, from documents to social media data to machine data, but I won’t drill into these here. Regardless of the type of search, in today’s era of distributed computing, in which there’s a need to explore a variety of data sources, information discovery is increasingly important.
The third trend in discovery analytics is a move to more embeddable system architectures. In parallel with the move to the cloud, architectures are becoming more service-oriented, and the interfaces are hardened in such a way that they can integrate more readily with other systems. For example, the visual discovery market was born on the client desktop with Qlik and Tableau, quickly moved to server-based apps and is now moving to the cloud. Embeddable tools such as D3, which is essentially a visualization-as-a-service offering, allow vendors such as Datameer to include an open source library of visualizations in their products. Lucene/Solr represents a similar embedded technology in the information discovery space.
VP and Research Director