11 Analytics Trends For The Digital Enterprise

by CXOtoday News Desk    May 20, 2015

analytics

A variety of factors are driving increased adoption and visibility for analytics. Organizations are recognizing the valuable insights that analytics can provide to help them compete. Research firm TDWI, has identified several interconnected trends in analytics that are relevant for companies advancing their analytics efforts. Here are 11 such trends to watch:

1. Ease of use. Analytics in the past, especially more advanced analytics, often required command-line code. Today, vendors have made interfaces easier to use and visualizations easier to construct. Better ease of use means organizations can succeed early and then build on that success to become more data driven.

2. The democratization and consumerization of analytics. More organizations are “democratizing” business intelligence (BI) and analytics to enable a broad range of non-IT users, from executive level to frontline personnel, to do more on their own with data access and analysis via self-service BI and visual data discovery.

3. Business analysts using more advanced techniques. Also connected to ease of use is the move from the statistician/modeler to a new user of predictive analytics—the business analyst. This frees the data scientist/statistician (typically a scarce resource) to build more complex and sophisticated models.

4. Newer kinds of analytics. In addition to predictive models, other kinds of analytics are emerging to drive business value. These include text analytics (analyzing unstructured text), social media analytics, geospatial analytics (analyzing location-related data), and clickstream analysis (analyzing customer behavior on websites). All of these techniques are starting to become mainstream and can provide important insight, either by themselves or in combination with other techniques. Others, such as the Internet of Things (IoT) analytics, are starting to gain steam.

5. Operationalizing analytics. When you operationalize something, you make it part of a business process. Operationalizing analytics is important because it makes analytics more actionable and thus drives more value. For example, a statistician might build a predictive model for churn. The model is then embedded in a system, and the model scores customers as they call in. Based on this score, information flows to a call center agent as part of a business process—say, to up- or cross-sell or take other measures to retain the customer. The agent doesn’t need to know how the model works but can make important use of the output for business advantage. Operationalizing analytics also makes it more consumable.

6. Big data. Referring to ever-increasing amounts of disparate data at varying velocities, big data is the buzzword du jour. However, it is much more than that. An important point about big data is that it is driving the use of existing techniques as well as the development of new techniques for data analysis. Big data is also driving the use of newer infrastructure such as Hadoop and multi-platform data warehouse environments that manage, process, and analyze new forms of big data, non-structured data, and real-time data. Leveraging big data processing tools allows analysts to perform queries on larger data sets, providing more robust models and deeper reports.

7. New development methods. Unlike with BI reporting, analytics often demands that users explore the data and try different visualizations and analytical techniques before they can arrive at insights. Analytics often demands a different methodology from what has been used for traditional IT projects to develop applications. Instead of “waterfall” methods and cycles that only deliver at the end of (usually) one long cycle, many organizations are employing agile methods. These faster, incremental cycles have guided organizations toward greater business-IT collaboration, faster and more iterative development cycles, and ultimately higher quality and satisfaction.

8. Open source. Open source is rapidly becoming popular for infrastructure as well as analytics. Hadoop is a prime example of how these technologies are becoming important in analytics. On the analytics front, the emergence of the R language also indicates the growing popularity of open source. The open source Python programming language is also increasingly popular for analytics. Open source is important because it enables the rising innovation happening around the analytics ecosystem.

9. The cloud. Although it has taken longer than some expected for the cloud to be used in BI, it is now entering the mainstream. One reason organizations are trying to move toward the cloud is to offset costs with zero capital expenditure on infrastructure, maintenance, and even personnel—to make BI more cost-effective. Additionally, deployment is faster. More often, companies are capturing big data in the cloud and experimenting with it there. Based on analysis, certain data is brought on premises to the data warehouse.

10. Mobile BI and analytics. The increasing adoption of mobile devices has opened up new platforms from which users can access data and both initiate and consume analytics. Executives on the go can apply analytics to gain deeper insight into business performance metrics, while frontline sales and service personnel can improve customer engagements by consuming data visualizations that integrate relevant data about warranty claims, customer preferences, and more.

 

11. Storytelling. As analytics and advanced analytics become more mainstream, being able to tell a data story with these technologies is becoming an important skill. A narrative that includes analysis can move beyond recounting facts to weave together pieces of analysis to make an impact and drive people to action.

TDWI researchers see two different kinds of data stories emerging. The first is one-time storytelling with a classic beginning, middle, and end, and often ends with a call to action. Modern storytelling is dynamic and often changes through time. It might use online dashboards or storyboards that are updated when new data arrives. The analysis is typically shared with others who comment and build an iterative and often interactive story.