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December 5, 2012 in Big Data, Business Analytics, Business Collaboration, Business Intelligence (BI), Business Performance Management (BPM), Cloud Computing, Customer Performance Management (CPM), Financial Performance Management (FPM), Information Management (IM), IT Performance Management (ITPM), Operational Performance Management (OPM), Social Media | Tags: Big Data, Business Analytics, CIO, Data Warehouse, Hadoop, Harvard Business Review, Predictive Analytics, Technology Innovation, Wall Street Journal | by Mark Smith | 3 comments
Big data was big news in 2012 and probably in 2013 too. The Harvard Business Review talks about it as The Management Revolution. The Wall Street Journal says Meet the New Boss: Big Data, and Big Data is on the Rise, Bringing Big Questions. Given big data’s popularity in the press, you might think that the technology market is only about big data and how companies use the vast and growing amount of data now available to organizations. While this technology can provide a significant opportunity, the reality is that just having big data does not provide an organization with the intelligence to be more efficient or grow market share. It can provide the foundation on which organizations can assemble technologies and applications that can help realize these opportunities, but organizations need to focus on the big picture, which encompasses additional layers of technology that work together with big data. Our recent benchmark research on business technology innovation found that big data is not the top priority for business or IT; analytics, collaboration, mobile and cloud computing are all more important. Organizations do believe that big data is very important (25%), but if they were pushed to prioritize technologies, it would not top the list.
The majority of the big data hyperbole focuses on the velocity, volume and variety of big data, which are important technology attributes for IT to deal with but deliver little to help business gain any opportunity for improvement. My colleague Tony Cosentino articulated this well in his blog (see Transforming Three Vs of Big Data into Three Ws of Business Analytics), which placed the pivotal value on the So What, Now What and Then What aspects of what business expects in time-to-value (TTV) aspects of big data. These factors are what business cares about in terms of analytic and information value from big data. Business is not concerned with the criteria IT uses to evaluate or determine which big data approach it is taking. Technology evaluations have fixated around the Vs of big data with no context of the Ws and no involvement from analysts who have to apply analytics or ensure the right information is made available in their business processes. That means IT organizations may be wasting their businesses’ time and resources. Our research into business technology innovation finds that lack of resources is the largest barrier (51%), and having IT expend significant quantities of time and resources on big data without a strong business context is a recipe for failure. Thankfully for many organizations, planning approaches for technology such as specialized DBMS (45%), in-memory databases (40%), data warehouse appliances (37%) and Hadoop (36%) requires a solid business case to move to full evaluation and deployment mode. If you hear the V pitch on big data, just ask about the W’s to get the conversation back to the business value.
When it comes to getting value from analyzing big data, our research found the three primary benefits organizations want were access and retention of data for analytics (29%), reducing the time required for analyzing data (13%) and increasing revenue (12%), which are more specific than benefits such as better communication, better management and tracking of initiatives and better organizational alignment. Those latter benefits are important, but organizations could also derive them from using business analytics. Ensuring you get the analytics value from big data also ensures you can mine or analyze big data for predictive analytics, forecasting and discovery, which, along with supporting taking action based on analytics, are the most critical business analytics needs in organizations today. With only a little more than half of organizations satisfied with their analytic processes, and 44 percent of organizations indicating the most time-consuming part of the analytics process is data-related tasks, IT must guarantee that business priorities of analysts who are held accountable for the information and metrics are included from the beginning.
Organizations must make sure to get business and IT together to determine whether they are getting the most value from the existing data stored in the organization. If you scale up the amount of data, is your organization prepared to take advantage of it and deliver business value in reduced time periods? Our research finds the most important change agents for selecting technology are a strong business improvement initiative (60%), drive to improve the quality of business process (57%) and operational efficiency and cost savings initiative (39%). Nothing should be different for big data, except that you should ensure you can use your IT resources efficiently and not build new silos of proprietary technology that require specialized resources that might not be aligned to the business value you expect to gain from the technology.
Big data can deliver big value if properly assessed and strategically applied, but like the data warehouse hype from almost 20 years ago, it will take time to ensure it can be properly applied for business and not just serve as a new technology initiative. For my final thoughts on the hyperbole of the Harvard Business Review and Wall Street Journal, they should use research and facts on what business and IT are doing today and what they need for working together to find value from big data since that is really the big deal.
CEO & Chief Research Officer
July 6, 2012 in Big Data, Business Analytics, Business Intelligence (BI), Business Performance Management (BPM), Cloud Computing, Customer Performance Management (CPM), Information Management (IM), IT Performance Management (ITPM), Operational Performance Management (OPM), Social Media | Tags: alteryx, Analytics, AVS, Big Data, Data Warehouse, Hadoop, HortonWorks, Information Management, Kognitio | by Mark Smith | Leave a comment
Kognitio has been serving the analytics and data needs of organizations for more than 20 years with an in-memory analytics platform that meets many of the big-data needs of today’s organizations. Kognitio Analytical Platform provides a unique massively parallel processing (MPP) in-memory database that can rapidly load data and calculate analytics; it is available both in an analytical software appliance and via cloud computing.
Its software can be installed on commodity x86 servers or used in the cloud on Amazon Web Services. The technology is fast to load with the company’s own tools or through data integration tools such as Informatica’s. Kognitio’s in-memory platform can take data from a disparate set of sources and process the data using analytics that can operate simultaneously across processors. As we noted last year Kognitio expanded to support MDX analytic query expressions, which through its Microsoft Excel interface can help conduct specific types of analytics faster than just using SQL. The challenge is that the use of MDX has not grown significantly among business analysts, nor has it been widely supported with other BI and analytics products. Kognitio has worked to reduce administrative complexity of its product; the appliance does not require a DBA to manage indexes or partitions.
In light of the significant growth in the use of Hadoop to process large volumes of data, Kognitio has formed a partnership with Hortonworks to integrate its technology with Hadoop through an accelerator. I assessed Hortonworks Hadoop Summit and how the use of Hadoop can accelerate the processing of analytics and data. Use of Hadoop is growing fast in big-data technology ecosystems; almost one-third of organizations now plan to use it, according to our big-data benchmark research. Kognitio has expanded its partnerships to reach analysts; for instance, it established a partnership with Alteryx, which provides a unique workflow and process-centric approach to analytics that I analyzed. It also announced a partnership and integration with Advanced Visual Systems for visualizing large volumes of data like consumer behavior and social media. These types of partnerships are critical for Kognitio, and it needs to explain their value better to prospective buyers and also promote them, which have yet to appear in the partner listing on its website.
Kognitio has struggled to grow as much as its unique technology probably deserved over the last ten years. Last year the U.K.-based company brought in new management to help guide its growth, especially in the United States. It also established new pricing for its technology based on the amount of memory used for processing the data. My analysis is that management needs to more directly publicize the performance and scalability of its approach compared to others to ensure it remains relevant in the conversation on big data and business analytics. Increasing its visibility will also help it reach the professionals who evaluate big-data technology, who according to our research are mostly in the IT organization.
Kognitio has some cost/benefit and computing efficiency advantages that should be very important to IT. While organizations usually know that analytics provide benefits, they also are looking for efficiency, streamlining their information architecture and providing operational support for business analysts, who are looking to be freed from manual work with preparing data and more automation so they can focus on analysis. If you are examining methods to increase the response time for analytics using big data and decrease the costs to do so, you should look at Kognitio.
Mark Smith – CEO & Chief Research Officer