Research Agenda: Big Data and Information Optimization in 2016


The big data market continues to expand and enable new types of analyses, new business models and new revenues streams for organizations that implement these capabilities. Following our previous research into big data and information optimization, we’ll investigate the technology trends affecting both of these domains as part of our 2016 research agenda.

A key tool for deriving value from big data is in-memory computing. As data is generated, organizations can use the speed of in-memory computing to accelerate the analytics on that data. Nearly-two thirds (65%) of participants in our big data analytics benchmark research identified real-time analytics as an important aspect of in-memory computing. Real-time analytics enables organizations to respond to events quickly, for instance, minimizing or avoiding the cost of downtime in manufacturing processes or rerouting deliveries that are in transit to cover delays in other shipments to preferred customers. Several big data vendors offer in-memory computing in their platforms.

Predictive analytics and machine learning also contribute to information optimization. These analytic techniques can automate some decision-making to improve and accelerate business processes that deal with large amounts of data. Our new big data benchmark research will investigate the use of predictive analytics with big data, among other topics. In combination with our upcoming data preparation benchmark research, we’ll explore the unification of big data technologies and the impact on resources and tools needed to successfully use big data. In our previous research, three-quarters of participants said they are using business intelligence tools to work with big data analytics. We will look for similar unification of other technologies with big data.

vr_Big_Data_Analytics_03_technology_for_big_data_analyticsThe emergence of the Internet of Things (IoT) – an extension of digital connectivity to devices and sensors in homes, businesses, vehicles and potentially almost anywhere – creates additional volumes of data and brings pressure for data in motion for both analytics and operations. That is, the data from these devices is generated in such volumes and with such frequency that specialized technologies have emerged to tackle these challenges. We’ll explore in depth the myriad issues arising from this explosion of connectivity in our benchmark research on the Internet of Things and Operational Intelligence this year.

Another key trend we will explore is the use of data preparation and information management tools to simplify accessibility to data. Data preparation is a key step in this process, yet our data and analytics in the cloud benchmark research reveals that data preparation requires too much time: More than half (55%) of participants said they spend the most time in their analytic process preparing data for analysis. Virtualizing data access can accelerate access to data and enables data exploration with less investment than is required to consolidate data into a single data repository. We will be tracking adoption of cloud-based and virtualized integration capabilities and increasing use of Hadoop as a data source and store for processing of big data. In addition, our research will examine the role of search, natural language and text processing.

We suggest organizations develop their big data competencies for continuous analytics – collecting and analyzing data as it is generated. It should start with establishing appropriate data preparation processes for information responsiveness. Data models and analyses should support machine learning and cognitive computing to automate portions of the analytic process. Much of this data will have to be processed in real time as it is being generated. All of these advances will need advanced methods for big data governance and master data management. We look forward to reporting on developments in these areas throughout 2016 in our Big Data and Information Optimization Research Agenda.

Regards,

David Menninger

SVP & Research Director

Big Data Research Agenda and Trends are Bolder in 2015


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 VentanaResearchLogo300pxbecomes 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 vr_BDI_03_plans_for_big_data_technologyspecialized 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,Ventana_Research_2014_Tech_Innovation_Award_Main 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.vr_oi_how_operational_intellegence_is_used 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 vr_Big_Data_Analytics_08_top_capabilities_of_big_data_analyticsand 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 vr_BDI_12_managing_big_data_integrationdata 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; vr_Info_Optimization_12_big_data_is_widely_usedalmost 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 vr_BDI_08_benefits_of_big_data_integrationinstrument 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.

Regards,

Mark Smith

CEO and Chief Research Officer