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2015-06-16 06:03:21
Narendra Mulani
June 15, 2015
While the interests in analytics and resulting benefits are increasing by the day, some businesses are challenged by the complexity and confusion that analytics can generate. Companies can get stuck trying to analyze all that s possible and all that they could do through analytics, when they should be taking that next step of recognizing what s important and what they should be doing for their customers, stakeholders, and employees. Discovering real business opportunities and achieving desired outcomes can be elusive.
To overcome this, companies should pursue a simpler path to uncovering the insight in their data and making insight-driven decisions that add value. Following are steps that we have seen work in a number of companies to simplify their analytics strategy and generate insight that leads to real outcomes:
Accelerate the data: Fast data = fast insight = fast outcomes. Liberate and accelerate data by creating a data supply chain built on a hybrid technology environment a data service platform combined with emerging big data technologies. Such an environment enables businesses to move, manage, and mobilize the ever-increasing amount of data across the organization for consumption faster than previously possible. Real-time delivery of analytics speeds up the execution velocity and improves the service quality of an organization. For example, a U.S. bank adopted such a technology environment to more efficiently manage increasing data volumes for its customer analytics projects. As a result, the firm experienced improved processing time by several hours, generating quicker insights and a faster reaction time.
Delegate the work to your analytics technologies. Uncovering data insights doesn t have to be difficult. Here are ways to delegate the work to your analytics technologies:
Next-Gen Business Intelligence (BI) and data visualization. At its core, next-gen business intelligence is bringing data and analytics to life to help companies improve and optimize their decision-making and organizational performance. BI does this by turning an organization s data into an asset by having the right data, at the right time and place (mobile, laptop, etc), and displayed in the right visual form (heat map, charts, etc) for each individual decision-maker, so they can use it to reach their desired outcome. When the data is presented to decision-makers in such a visually appealing and useful way, they are enabled to chase and explore data-driven opportunities more confidently.
For example, a financial services company applied BI and data visualization to see the different buckets of risk across its entire loan portfolio. After analyzing its key data and displaying the results via visualizations, the firm identified the areas in the U.S. where there were high delinquency rates, explored tranches based on lenders, loan purposes, and loan channels, and viewed bank loan portfolios. Users were also able to interact with the results and query the data based on their needs select different date ranges, FICO scores, compare lenders and loan types, etc. Due to the flexibility and data exploration capabilities of the interactive BI and visualization solution, insight-driven decisions could be made and actions could be pursued that would benefit the business.
Data discovery. Data discovery can take place alongside outcome-specific data projects. Through the use of data discovery techniques, companies can test and play with their data to uncover data patterns that aren t clearly evident. When more insights and patterns are discovered, more opportunities to drive value for the business can be found. For instance, a resources company was able to predict which pipelines are most risky from both physical and atypical threats through data discovery techniques. Due to the insights gained, the firm was able to prioritize where they should invest funds for counter-failure measures and maintenance repairs.
Analytics applications. Applications can simplify advanced analytics as they put the power of analytics easily and elegantly into the hands of the business user to make data-driven business decisions. They can also be industry-specific, flexible, and tailored to meet the needs of the individual users across organizations from marketing to finance, and levels from C-suite to middle management. For example, an advanced analytics app can help a store manager optimize his inventory and a CMO could use an app to optimize the company s global marketing spend.
Machine learning and cognitive computing. Machine learning is an evolution of analytics that removes much of the human element from the data modeling process to produce predictions of customer behavior and enterprise performance. As described in the Intelligent Enterprise trend in the Accenture Technology Vision 2015 report: With an influx of big data, and advances in processing power, data science and cognitive technology, software intelligence is helping machines make even better-informed decisions. As an example, a retailer combined data from multiple sales channels (mobile, store, online, and more) in near real-time and used machine learning to improve its ability to make more personalized recommendations to customers. With this data-driven approach, the company was able to target customers more effectively and boost its revenues.
Recognize that each path to data insight is unique. The path to insight doesn t come in one single form. There are many different elements in play, and they are always changing business goals, technologies, data types, data sources, and then some are in a state of flux. Another main component of a company s analytics journey depends on the company s culture itself: is it more conservative or willing to take chances? Does it have a plethora of existing data and analytics technologies to work with, or is it just starting out with its first analytics project? No matter what combination of culture and technology exists for a business, each path to analytics insight should be individually paved with an outcome-driven mindset.
To do this, companies can take two approaches depending on the nature of the business problem. First, for a known problem with a known solution such as customer segmentation and propensity modeling for targeted marketing campaigns the company could take a hypothesis-based approach by starting with the outcome (e.g. cross-sell/up-sell to existing customers), pilot and test the solution with a control group and then scale broadly across the customer base. Second, for a known problem area, fraud for example, but with an unknown solution, the company could take a discovery-based approach to look for patterns in the data to find interesting correlations that may be predictive for instance, a bank found that the speed at which fields were filled out on its online forms was highly correlated with fraudulent behavior. Of note, when determining which problem to address, companies should first focus on the one that can offer the highest value, then it can choose a hypothesis-based or discovery-based approach based
on the degree of institutional knowledge it has to solve that kind of problem.
Once insights are uncovered, the next step is for the business, of course, to make the data-driven decisions that place action behind the data. It is possible to uncover the business opportunities in your data and increase data equity, simply.
Narendra Mulani is the senior managing director of Accenture Analytics.