In this special guest feature, Andrew Pease, Principal Business Solutions Manager at SAS, discusses why enterprise analytics must incorporate scale and its relation to speed and innovation within an organization. After 14 years in various roles at SAS, Andrew is currently responsible for advanced analytics in the Center of Excellence. Andrew helps financial institutions, major retailers, pharmaceuticals, manufacturers, utilities and public sector to understand and use powerful analytic techniques such as decision management, predictive modelling, time-series forecasting, optimization, and text mining.
In my travels across Europe for my job, I’ve taken my share of trains. I particularly enjoy the sleek, high-speed TGV, which travels around France and to 15 countries at up to 320 kilometers per hour. Not only is the train itself blindingly fast, but streamlined processes further accelerate travel. These include:
- Automated check-in.
- Seamless handoffs from one train to another.
- Convenience and flexibility with trains every half hour.
- Direct travel from city-center to city-center.
A generation ago, if I had a meeting in Brussels, I would have had to fly there from Paris. The trip would have easily taken half the day and involved travel to the airport, check-ins, security, and inflexible schedules. The TGV takes me door-to-door in two hours.
The comparison between the TGV and air travel presents a metaphor for scalable, enterprise analytics. While airlines can’t scale, high-speed rail, with its user friendly service, can. Scale means speed, and the ability to meet growing demands without having to increase resources equivalently.
Decisions at scale
There are myriad ways to deliver enterprise analytics at scale.
Analytics drive better decisions in an increasingly competitive market. For years, organizations have used analytics to find insights to drive strategy. Which markets to focus on? Which products? And which customers?
Unfortunately, in enterprise analytics initiatives, the small number of data scientists has been a bottleneck. As an organization’s appetite for analytics grows, we need systems and processes that will allow data scientists to spend more time innovating and less time maintaining operational processes.
At the same time, analytically-derived insights have been difficult to consistently push to where they have the most impact: Operational touchpoints with customers, suppliers and within the organization. My employer, longtime analytics supplier SAS, and other companies are addressing these challenges.
Five dimensions of scale
But in a field as sophisticated as analytics the range of needed scalability is vast, spanning various data types, analytics software options and necessary processes such as data preparation. Consider the critical areas where scalability is most important:
- People. Scale can mean making evaluation tasks available to more types of business users. With growing volumes of data, opening up powerful analytics to an army of knowledge-hungry business analysts can help uncover pockets of potential untapped by data scientists. New interactive data exploration, visual predictive analytics, automated modelling and decision management packages make powerful analytics easy to execute and interpret. More analysts can better understand the underlying methods and put them to better use.
- Data preparation. Scale can mean data preparation happens in a robust, automated way that’s uniquely relevant for the kinds of innovative analytics that motivate data scientists and that help organizations drive competitive edge. Data preparation is easier and more automatic than ever.
- Scale can mean going closer to where data is actually created, then using analytics as an intelligent filter to capture only relevant data. In an exploding sea of big data (Internet of things, sensors, social media, transactions, flash trading, etc.) analytics on data “in motion” performs streaming analytics, becomes a vital part of any organizations’ big data strategy.
- Data types. Scale can imply expanding the types of data your organization can exploit. Unstructured data such as documents and web pages can contain vital information. Storing textual data in a traditional database or appliance is neither efficient nor practical. Text analytics solutions should combine the right storage medium with relevant analytic techniques.
- Analytics types. Last but not least, scale can mean using machine learning to drive unprecedented innovation by expanding the analytic and algorithmic possibilities available to data scientists and then quickly and intuitively disseminating the insights.
All of these analytics use cases are about scaling analytics within organizations to more effectively drive analytics into action. With an exciting range of new operational analytics solutions, enterprises will be prepared to make decisions at scale, while bringing unprecedented ease, sophistication and automation to operational analytics.
Sign up for the free insideBIGDATA newsletter.