Monday marked the kick-off of Gartner BI in Las Vegas. The opening keynote certainly laid out what seemed to be a theme for the conference, “The Future of Your Business: Transparent, Decisive, and Personalized.” However, there was a lingering disconnect between the morning keynote and the way the evening session represented the market, siloed and artificially limited to vendors’ capabilities, in research that looks exclusively at Business Intelligence, Data Warehousing or Advanced Analytics. If the future of the business is to deliver transparency, decisive and personalized capabilities, isn’t it imperative to have a platform that is not siloed (e.g., just connecting to data or storing it) to only a portion of the Analytics Value Chain? Please read on to see how the day played out …
During the Keynote, Twitter was buzzing with conversations all about it:
The messaging was all around transparency and a shift in focus towards business outcomes as opposed to a pure technology focus. Collaboration was a theme emphasized heavily. The three analysts present – Frank Buytendijk, Kurt Schlegel, and Rita Sallam – talked a lot about how real business value would come from utilizing different types of information for making business decisions. They spoke about how UPS makes routing decisions to save $50M annually, how data management is giving way to decision management, and how business outcomes focused projects lead to better technology decisions. Finally – all goodness from my perspective.
In the afternoon, I had the privilege to co-present with our customer Roselyne Christien, Senior Business Analyst from Fidelity’s FIS team responsible for fraud prevention and safeguarding more than 47 million cardholders from fraud. FIS cannot afford to not respond to fraud threats in real-time. As a global technology leader working with more than 14,000 clients in 100+ countries, it must act with speed and agility. She outlined the many challenges they faced in their previous data environment. Roselyne shared a compelling and prescriptive approach used by FIS and how the Actian Analytics Platform has changed their fraud prevention analytics. A phenomenal example of how analytics enabled business processes are adapting to the realities of the market by using a combination of large/diverse data sets of discovery analytics combined with the need for low-latency, time-sensitive analytics that are accurately predicting the Segment of 1™
Prior to 2009 FIS Fraud Analytics used a 10g Oracle Data Warehouse, which became an inadequate silo for their large and growing data volume and frequent loads. They needed a system accessible to a broader set of business users as opposed to one that could only be deciphered by IT or data scientists. The nature of their data collection demanded a more robust and high-performance ad hoc analytic environment, because as Roselyne aptly stated in her presentation, in Fraud Analytics:
“Very simple questions turn into very complex queries.”
They needed to be able to build new types of queries on the fly that could deliver a quick response to fraud patterns. For example, they needed queries that would raise red flags when the place of purchase seemed questionable and the amount uncharacteristic of the account holder.
When looking into a new big data platform selection their objectives were clear. They needed an architecture that would handle growing data volumes and process highly complex queries at high levels of performance. They also needed it to scale linearly to allow for increasing business volume and demand for analytics performance, and to be a cost-effective solution.
This meant that they needed the following: Data loads and extracts with speed and functionality, rapid queries with the ability to handle complex SQL, and the ability to scale linearly and add notes. High security, high Stability and disaster recovery, and ongoing support were essential. And they demanded Admin Tools including diagnostics, statistics, workflow management, user management and storage management.
FIS chose the Actian Analytics Platform for its ability to support large data volumes while sustaining high performance. They wanted fast-running analytics dashboards to monitor customers’ portfolios daily. Actian also gave them the ability to run complex queries (including time and velocity components) with excellent performance. In short we’ve provided the flexible and robust platform they need to support future growth and accommodate a variety of user roles and skill levels.
The FIS case study also displays that having a clear business outcome in mind before making technology selections is the right approach. Of course, price and performance were a huge factor in their choice as well, but most importantly they were looking for a partnership and collaboration that would enable them to meet their business’ requirements and use cases. We are dedicated to continually partner with our customers and improve the Actian Analytics Platform to offer superior support and scalability.
The day closed with a keynote about three of the Gartner Magic Quadrants (BI, DW DBMS and newly minted Advanced Analytics) and Actian is proud to be positioned as a “visionary” for Data Warehouse DBMS. During this session, it was very interesting to see the contrast with the morning keynote, which was focused on collaborative analytics and decision management. The MQ discussion was very focused on specific technology areas without the connective tissue to the future that was discussed in the morning keynote. At the panel presentation, two people sitting near me (and I) repeatedly mentioned that it was surprising that the panel led back to point capabilities instead of the collaborative requirements outlined in the morning keynote. This transitionary dilemma is exactly what customers are grappling with today and as they struggle to deliver on the business outcomes required by the market, their customers, their competitors and sometimes even their own internal departments. It often results in what we call the “do nothing” loop as Clay Christensen outlines in his book “The Innovators Dilemma”. We already have the evidence, companies using predictive analytics to create transformational value (think of Amazon and their recommendation engine) are outpacing their competitors. That, however, will be the topic of my next blog …
For now, I am looking forward to another day at the conference and hopefully speaking to those of you who are at the conference at our booth #210.