The explosion of data collection promises so much. Where once companies were only able to collect structured data from limited sources; now they can mine everything from social media to call center transcripts, with more and more big data tools available to process a far wider range of information. So, those who collect the most data are best positioned to gain a competitive advantage, right?
Not so fast. When companies try to use their newfound wealth of data for a specific purpose (e.g., to decide whether to launch a new product), they invariably discover some glaring gaps in the information they require. Why are companies still finding themselves short of the right data to answer key business questions? The answer is that they didn’t design their data collection processes with those exact questions in mind.
Getting past this challenge requires a shift in mindset. Data collection isn’t a goal in its own right, but a means to an end. And getting to that end – where the company is able to use data to answer more of its questions – requires applications that are designed accordingly. The applications still need to meet users’ needs, but their data collection features need to be tailored to produce the answers to the questions the business is actually asking (or expecting to ask).
To be sure that the right data emerges at the end of the collection process, companies need to address this problem as a supply chain issue.
By quantifying every aspect of their businesses’ actions, interactions and processes, firms should be able to decide which questions they need to answer in order to be smarter. The necessary data required for that will then be clearer, so the challenge becomes how to collect it or even how to create it.
And help is available for that. Software vendors offer tools that enable companies to extract data more easily from their packaged software applications. New instruments can be added to existing applications to capture additional data. For example, Netflix tracks how its customers interact with its streaming movies, studying metrics such as when viewers pause the movie and which scenes are watched repeatedly. This data has helped the company improve its recommendation engine.
Sensor technology enables companies to create and collect information from physical environments. Look at UPS: The company’s in-vehicle sensors have tracked movements of its trucks so precisely that the company determined that making left turns slows deliveries and increases fuel costs. By redesigning routes to minimize left-hand turns, it has saved 9 million gallons of gas per year and improved customer service by predicting more accurate delivery times.
The process of quantification has evolved quickest when it comes to consumer behavior, where opportunities for data collection have never been richer. For instance, there are social media applications where consumers provide their own data, like the online reviews found at Yelp. Another example is in the area of personal fitness, where applications, such as Nike+ FuelBand and Fitbit, collect data on everything from consumers’ calorie burning to their sleep patterns and diet.
Other data sources for analysis include unstructured data, such as email and tweets, as well as audio files from call center conversations. And companies should not neglect the data collected by others with which they work. Service providers, social networks and even software providers running packages in businesses’ data centers may all be useful sources of information.
But only those companies that know what kind of questions they want answered can judge which of these data sources will truly help them. So the supply chain continues; having identified where the right data will come from, the next step is to organize and manipulate it for analysis.
Companies that expressly design the supply chain with specific analytics in mind will find it more straightforward to bring the data together to answer those questions because there are far fewer information gaps to contend with. The ideal is a virtuous feedback loop in which companies collect, analyze and respond to data in an increasingly agile manner; the questions are revisited and revised on a regular basis as business conditions change and the company’s strategy evolves.
However, getting to that stage will require IT to work much more closely with business leaders. These leaders are responsible for deciding which questions are most important to the business, and surprisingly few executives have processes in place for documenting those questions and sharing them. By setting up such processes, IT and business can work together to explore all the possible sources of data that might provide answers. They will also need to establish how those answers will be distributed within the business so that they are actionable.
This is a cultural shift for most businesses. In order to design effectively for analytics, the roles of the IT organization, which provides the data, and the business, which consumes it, need to be much more blurred. The aim is an enterprise culture where IT and business work together closely to deliver insights that produce a real competitive edge.
Some companies have addressed this objective by creating a new executive role: a chief data officer who can champion the collection, prioritization, distribution and analysis of data. While this push from the top has been effective for many organizations, the wider goal is a culture where all employees are more data-aware. Data technology has evolved but people still must decide what data is useful and how to harvest it.
The prize is a valuable one. Already, data is available from more diverse sources than ever imagined for businesses to analyze and apply. But businesses need the right data, and companies that design their applications, processes and products to deliver data-driven insights will be the ones that succeed and prosper.
(Author's note: To read more about “design for analytics” and other trends IT and business leaders need to prepare for, download Accenture’s newly released Technology Vision 2013.)
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