‘Beautiful’ Data is Useful Data

useful data
‘Beautiful’ Data is Useful Data

Organizations are finding it easier to acquire data and flow it through their systems. However, it’s likely to be piling up, either unused or misunderstood. Managing, maintaining and storing increasingly larger volumes and varieties of data has a cost – and it can be expensive. Whether storing onsite or in the cloud, it adds up.

In a recent post, Helen Mayhew, Tamim Saleh, and Simon Williams, all with McKinsey, say data analytics should work for you — “instead of the other way around.” This advice has the ring of good money management: the smartest approach is being able to put money to work for you, versus having to work too hard for money.

They offer up the following advice for making your data work harder for you:

Ask the right questions: Don’t go out trying to collect as much data as possible, only hoping something will turn up in the process. And, as the data rolls in, don’t be too vague with your line of questioning. “Clarity is essential,” Mayhew, Saleh, and Williams state. “Examples of good questions include ‘how can we reduce costs?’ or ‘how can we increase revenues?’ Even better are questions that drill further down: ‘How can we improve the productivity of each member of our team?’ ‘How can we improve the quality of outcomes for patients?’ ‘How can we radically speed our time to market for product development?’”

Think really small — and very big: Break data into the smallest component parts, and resync them together in accordance with business needs. “Companies can identify small points of difference to amplify and exploit,” the McKinsey team observes. “The impact of ‘big data’ analytics is often manifested by thousands—or more—of incrementally small improvements. If an organization can atomize a single process into its smallest parts and implement advances where possible, the payoffs can be profound. And if an organization can systematically combine small improvements across bigger, multiple processes, the payoff can be exponential.”

Low-quality data may have its uses: Data may be cast aside because it is perceived as incomplete or of poor quality. However, there is value in less-than-stellar data as well. “We can achieve sharper conclusions if we make use of fuzzier stuff.  In fact, while hard and historical data points are valuable, they have their limits. Just because information may be incomplete, based on conjecture, or notably biased does not mean that it should be treated as ‘garbage.’ Soft information does have value. Sometimes, it may even be essential, especially when people try to ‘connect the dots’ between more exact inputs or make a best guess for the emerging future.”

Keep an open mind. Data sets may seem unrelated – such as HR information and production data – but may lead to new insights when the dots are connected.  “Combining sources of information can make insights sharper,” the McKinsey authors write. “Too often, organizations drill down on a single data set in isolation but fail to consider what different data sets convey in conjunction. Additional untapped value may be nestled in the gullies among separate data sets.”

Build a multiskilled team: “Assembling a great team is a bit like creating a gourmet delight—you need a mix of fine ingredients and a dash of passion,” the McKinsey team relates. “Key team members include data scientists, who help develop and apply complex analytical methods; engineers with skills in areas such as microservices, data integration, and distributed computing; cloud and data architects to provide technical and systemwide insights; and user-interface developers and creative designers to ensure that products are visually beautiful and intuitively useful. You also need translators — men and women who connect the disciplines of IT and data analytics with business decisions and management.”

Make your output usable—and “beautiful”: Top-level decision-makers won’t respond to quantitative reports – they need information that tells a story and describes how value can be added to their businesses. “While the best algorithms can work wonders, they can’t speak for themselves in boardrooms,” Mayhew, Saleh, and Williams explain. “Data scientists too often fall short in articulating what they’ve done. Analytics should be consumable, and best-in-class organizations now include designers on their core analytics teams. We’ve found that workers throughout an organization will respond better to interfaces that make key findings clear and that draw users in.”

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