Enterprises are flooded daily with information from every part of their business, generated from email, social media, websites, mobile apps, and more.
Industry estimates peg data growth at between 30% and 50% annually, resulting in not just terabytes but petabytes of data for many organisations. The problem is clearly not a lack of data. Instead, businesses are suffering from a lack of the "right" data.
In a recent Accenture Analytics survey of 600 executives representing large organisations in the UK and US, just 40% said their current analytics programs are able to identify actionable data. And only 20% are "very satisfied" with the business outcomes from their existing analytics programs. These are underwhelming responses to say the least.
Having the right data helps enterprises make better decisions. Getting it, however, requires a fundamental shift in how applications are built, configured, instrumented and updated. While applications must meet functionality needs, they also need to be designed to deliver data that answers the enterprise’s key questions.
The need for enterprises to “design for analytics” is identified in the Accenture Technology Vision 2013 report – a look at the future of enterprise IT – as one of the key ways companies can take advantage of technology and software to improve their competitiveness, operations and business results.
View data as a supply chain, not a warehouse
Many companies capture data without specific questions in mind. So, when data is analysed as inputs for strategic business decisions – such as entering a new market or pricing a new product – glaring information gaps can arise that result in missed opportunities.
Technology is no longer the barrier; it’s about having the strategic foresight to formulate the right questions. The result is the first stage of a data supply chain, where applications serve not only the users, but the business as it seeks answers to its most important questions.
To shift the focus from designing applications for function to designing them for analytics, CIOs can begin the process by doing the following.
Figuring out not only how to collect data, but how to create it
Many software suppliers are already making it easier with application programming interfaces (APIs) that allow data to be more easily extracted from software products, including software applications. This puts the onus on companies to figure out what data they should be gathering from their systems to answer the company’s most critical questions.
Some companies are also adding instrumentation to their custom applications, designing teams to collect and report transactions, activities or logs, and using sensor technology to fill data gaps as they arise.
As an example, UPS developed a system of in-vehicle sensors and handheld computers to track information about its shipments and the movements of its vehicles. UPS found that making left turns (in the US) slowed deliveries and increased fuel costs – information that resulted in a savings of nine million gallons of fuel annually.
Quantifying consumers and the enterprise
The ability to cultivate and harvest information for sales and marketing represents a rich opportunity for companies to grab data needed to resolve questions about consumers that have long gone unanswered.
With industry estimates projecting anywhere between 30 and 50 billion connected devices by 2020, there is and will be an abundance of data generated through various social media, mobile capabilities and sensor technology, as well as unstructured data. But companies need to determine the right data for them and then initiate the processes for acquiring it.
Creating a data supply chain
Once the right data is identified, it should be treated like automobile components from multiple suppliers that come together on an assembly line.
Data can be manipulated as it filters through the supply chain, added to other pieces of data, updated with more recent data, and transformed into new products.
By collecting data with the end purpose in mind, companies harvest better data and greater insight – and can later revisit their questions on a periodic basis to access new data as changing business conditions and strategies dictate.
Changing the corporate culture
This means making the enterprise culture more insight-driven by blurring the lines between business functions and IT, and championing the drive to collect better, fresher data.
By deploying these capabilities, businesses will get closer to the goal of being completely insight-driven. This means evolving applications and products beyond user functionality and having them actively feed analytics so as to produce not just more data, but the kind of data that will answer the business’s most important questions.
Paul Daugherty is chief technology officer at Accenture.
To read more about Design for Analytics, download the Accenture Technology Vision 2013 report here.
This was first published in October 2013