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In many organizations, advanced analytics groups and IT are separate, and there often is a chasm of understanding between them, as I have noted. A key finding in our benchmark research on big data analytics is that communication and knowledge sharing is a top benefit of big data analytics initiatives, but often it is a latent benefit. That is, prior to deployment, communication and knowledge sharing is deemed a marginal benefit, but once the program is deployed it is deemed a top benefit. From a tactical viewpoint, organizations may not spend enough time defining a common vocabulary for big data analytics prior to starting the program; our research shows that fewer than half of organizations have agreement on the definition of big data analytics. It makes sense therefore that, along with a technical infrastructure and management processes, explicit communication processes at the beginning of a big data analytics program can increase the chance of success. We found these qualities in the Chorus platform of Alpine Data Labs, which received the Ventana Research Technology Innovation Award for Predictive Analytics in September 2014.
Alpine Chorus 5.0, the company’s flagship product, addresses the big data analytics communication challenge by providing a user-friendly platform for multiple roles in an organization to build and collaborate on analytic projects. Chorus helps organizations manage the analytic life cycle from discovery and data preparation through model development and model deployment. It brings together analytics professionals via activity streams for rapid collaboration and workspaces that encourage projects to be managed in a uniform manner. While activity streams enable group communication via short messages and file sharing, workspaces allow each analytic project to be managed separately with capabilities for project summary, tracking and data source mapping. These functions are particularly valuable as organizations embark on multiple analytic initiatives and need to track and share information about models as well as the multitude of data sources feeding the models.
The Alpine platform addresses the challenge of processing big data by parallelizing algorithms to run across big data platforms such as Hadoop and making it accessible by a wide audience of users. The platform supports most analytic databases and all major Hadoop distributions. Alpine was an early adopter of Apache Spark, an open source in-memory data processing framework that one day may replace the original map-reduce processing paradigm of Hadoop. Alpine Data Labs has been certified by Databricks, the primary contributor to the Spark project, which is responsible for 75 percent of the code added in the past year. With Spark, Alpine’s analytic models such as logistic regression run in a fraction of the time previously possible and new approaches, such as one the company calls Sequoia Forest, a machine learning approach that is a more robust version of random forest analysis. Our big data analytics research shows that predictive analytics is a top priority for about two-thirds (64%) of organizations, but they often lack the skills to deploy a fully customized approach. This is likely a reason that companies now are looking for more packaged approaches to implementing big data analytics (44%) than custom approaches (36%), according to our research. Alpine taps into this trend by delivering advanced analytics directly in Hadoop and the HDFS file system with its in-cluster analytic capabilities that address the complex parallel processing tasks needed to run in distributed environments such as Hadoop.
A key differentiator for Alpine is usability. Its graphical user interface provides a visual analytic workflow experience built on popular algorithms to deliver transformation capabilities and predictive analytics on big data. The platform supports scripts in the R language, which can be cut and pasted into the workflow development studio; custom operators for more advanced users; and Predictive Model Markup Language (PMML), which enables extensible model sharing and scoring across different systems. The complexities of the underlying data stores and databases as well as the orchestration of the analytic workflow are abstracted from the user. Using it an analyst or statistician does not need to know programming languages or the intricacies of the database technology to build analytic models and workflows.
It will be interesting to see what direction Alpine will take as the big data industry continues to evolve; currently there are many point tools, each strong in a specific area of the analytic process. For many of the analytic tools currently available in the market, co-opetition among vendors prevails in which partner ecosystems compete with stack-oriented approaches. The decisions vendors make in terms of partnering as well as research and development are often a function of these market dynamics, and buyers should be keenly aware of who aligns with whom. For example, Alpine currently partners with Qlik and Tableau for data visualization but also offers its own data visualization tool. Similarly, it offers data transformation capabilities, but its toolbox could be complimented by data preparation and master data solutions. This emerging area of self-service data preparation is important to line-of-business analysts, as my colleague Mark Smith recently discussed.
Alpine Labs is one of many companies that have been gaining traction in the booming analytics market. With a cadre of large clients and venture capital backing of US$23 million in series A and B, Alpine competes in an increasingly crowded and diverse big data analytics market. The management team includes industry veterans Joe Otto and Steve Hillion. Alpine seems to be particularly well suited for customers that have a clear understanding of the challenges of advanced analytics and are committed to using it with big data to gain a competitive advantage. This benefit is what organizations find most in over two thirds (68%) of organizations according to our predictive analytics benchmark research. A key differentiator for Alpine Labs is the collaboration platform, which helps companies clear the communication hurdle discussed above and address the advanced analytics skills gap at the same time. The collaboration assets embedded into the application and the usability of the visual workflow process enable the product to meet a host of needs in predictive analytics. This platform approach to analytics is often missing in organizations grounded in individual processes and spreadsheet approaches. Companies seeking to use big data with advanced analytics tools should include Alpine Labs in their consideration.
VP and Research Director
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