One of the key findings in our latest benchmark research into predictive analytics is that companies are incorporating predictive analytics into their operational systems more often than was the case three years ago. The research found that companies are less inclined to purchase stand-alone predictive analytics tools (29% vs 44% three years ago) and more inclined to purchase predictive analytics built into business intelligence systems (23% vs 20%), applications (12% vs 8%), databases (9% vs 7%) and middleware (9% vs 2%). This trend is not surprising since operationalizing predictive analytics – that is, building predictive analytics directly into business process workflows – improves companies’ ability to gain competitive advantage: those that deploy predictive analytics within business processes are more likely to say they gain competitive advantage and improve revenue through predictive analytics than those that don’t.
Topics: Big Data, Microsoft, Predictive Analytics, SAS, Social Media, alteryx, advanced analytics, Customer Performance, Operational Performance, organizational transformation, Analytics, Business Analytics, Business Intelligence, Business Performance, Operational Intelligence, Oracle, Information Optimization, SPSS, Rapidminer
Our benchmark research into predictive analytics shows that lack of resources, including budget and skills, is the number-one business barrier to the effective deployment and use of predictive analytics; awareness – that is, an understanding of how to apply predictive analytics to business problems – is second. In order to secure resources and address awareness problems a business case needs to be created and communicated clearly wherever appropriate across the organization. A business case presents the reasoning for initiating a project or task. A compelling business case communicates the nature of the proposed project and the arguments, both quantified and unquantifiable, for its deployment.
Topics: Big Data, Microsoft, Predictive Analytics, SAS, Social Media, alteryx, advanced analytics, Customer Performance, Operational Performance, organizational transformation, Analytics, Business Analytics, Business Intelligence, Operational Intelligence, Oracle, Information Optimization, SPSS, Rapidminer
Many businesses are close to being overwhelmed by the unceasing growth of data they must process and analyze to find insights that can improve their operations and results. To manage this big data they find a rapidly expanding portfolio of technology products. A significant vendor in this market is SAS Institute. I recently attended the company’s annual analyst summit, Inside Intelligence 2014 (Twitter Hashtag #SASSB). SAS reported more than $3 billion in software revenue for 2013 and is known globally for its analytics software. Recently it has become a more significant presence in data management as well. SAS provides applications for various lines of business and industries in areas as diverse as fraud prevention, security, customer service and marketing. To accomplish this it applies analytics to what is now called big data, but the company has many decades of experience in dealing with large volumes of data. Recently SAS set a goal to be the vendor of choice for the analytic, data and visualization software needs for Hadoop. To achieve this aggressive goal the company will have to make significant further investments in not only its products but also marketing and sales. Our benchmark research on big data analytics shows that three out of four (76%) organizations view big data analytics as analyzing data from all sources, not just one, which sets the bar high for vendors seeking to win their business.
Topics: Big Data, Predictive Analytics, SAS, Event Stream, Operational Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, CIO, Customer & Contact Center, Data Management, Information Applications, Information Management, Location Intelligence, Operational Intelligence, Discoveryf
Ventana Research recently completed the most comprehensive evaluation of mobile business intelligence products and vendors available anywhere today. The evaluation includes 16 technology vendors’ offerings on smartphones and tablets and use across Apple, Google Android, Microsoft Surface and RIM BlackBerry that were assessed in seven key categories: usability, manageability, reliability, capability, adaptability, vendor validation and TCO and ROI. The result is our Value Index for Mobile Business Intelligence in 2014. The analysis shows that the top supplier is MicroStrategy, which qualifies as a Hot vendor and is followed by 10 other Hot vendors: IBM, SAP, QlikTech, Information Builders, Yellowfin, Tableau Software, Roambi, SAS, Oracle and arcplan.
Topics: MicroStrategy, Mobile BI, Mobile Business Intelligence, Pentaho, Sales Performance, SAP, SAS, Tableau, Jaspersoft, mobile analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, Customer & Contact Center, IBM, Information Builders, Oracle, Workforce Performance, Yellowfin, Actuate, BIRST, Roambi, Value Index, arcplan, Logi Analytics, Qlik
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.
Topics: Big Data, Datameer, Sales Performance, SAS, Supply Chain Performance, Teradata, alteryx, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, Governance, Risk & Compliance (GRC), Hadoop, IBM, Information Applications, Location Intelligence, Operational Intelligence, Workforce Performance