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Getting advantage of your data abundance

Data has exploded. It continues at an exponential rate. Over 92% of all the data ever generated has been created in the past 5 years. Increasing popularity of Video, IoT, messaging and the cloud will only exacerbate the problem. The legacy mindset of “keep everything forever and look into it when there is time” is no longer viable. Organisations suspect that almost a third of their data is inaccurate, and 70% said they don’t have the direct control they need to impact strategic objectives. Incorrect ownership (69%), lack of trust in data (49%) and information overload (65%) are the three most common factors preventing businesses from using data to their advantage.
There are many ways to put data to work, and companies, and especially their leaders, are advised to explore as many of them as they can. Each presents distinct opportunities for competitive advantage and potential profits, from customer service and product improvements to new revenue streams to possible industry game changers. At the same time, each presents challenges that must be tackled to be appreciated.

Using data to its full potential is about combining the best of management and technology. A team of data scientists may employ a series of clever analyses to yield an important insight, but that insight will soon start collecting dust if others in the organization don’t carry it forward by understanding and making a critical decision to build it into a product or leverage it in interactions with customers. Putting data to work includes the whole sequence, from data to insight to profit.

But how do you start?

It’s easy to know which data to focus on when you have a specific use case you’d like to work on. Yet, many company leaders realise undiscovered potential of data they hold but don’t know what are potential use cases.
In his article for Harward Business Review, Thomas C. Redman suggests that companies should explore seven methods to put data to work to learn which ones work best for their business:

  • Make better decisions. First, use better (more relevant, more accurate) data when making decisions, up and down the organization chart. I’ve not worked with or heard of a company that didn’t freely admit that it needed to make better decisions — and many push hard to improve. But incorporating more and better data into decision making can be difficult. You must learn to understand variation, to combine data from different sources, and to drive decision making to the lowest possible level. By taking the time to learn these skills, though, you can use data to reduce uncertainty, increasing the chances of making sound decisions.
  • Innovate products, services, and processes. Use data to uncover hidden insights, and use those insights to create or improve products, services, and processes. For example, at Morgan Stanley, Jeff McMillan and his team aim to improve working relationships with their wealth management clients by analyzing everything from client goals and portfolios to available investment products to email. An algorithm then takes this information and suggests actions, at which point advisors choose the best ones to suggest to their clients. McMillan encourages advisors to “imagine you have a conversation at 6:00 PM every evening with a Harvard MBA with 800 years’ experience. You tell her what you’re thinking about, and she thinks through your clients’ opportunities all night long. In the morning, she presents you with a list of your 10 best actions for the day. Wouldn’t that help you make your clients happier?” Their goal is to develop personalized strategies for each client based on far more data and analytic horsepower than any financial adviser could marshal alone.
  • Informationalize products, services and processes. Build more data into what you offer customers, so you make existing products more valuable. Automobile manufacturers have a history of working on this by adding warning lights, GPS, distance-to-empty gas tank notifications, and other features almost seamlessly. I’ve yet to run across a product or process that wouldn’t benefit from more data.
  • Improve quality, eliminate costs, and build trust. Proactively address quality by finding and eliminating the root causes of errors. Virtually everything a company does, from delivering products to running the place, uses enormous quantities of data. But bad data makes this work more difficult and increases costs — up to 20% of revenue! You can’t expect someone to factor data they don’t trust into an important decision. Take steps to actively track down data quality issues and eliminate their root causes.
  • Provide content. Sell or license new, richer, or more targeted data. All customers depend on content, and thousands of companies, such as Bloomberg and 23andMe, aim to fill the need. Still, most companies don’t think much about selling their data. But doing so can provide great opportunity. For example, car insurance companies discovered a relatively simple piece of data they could sell: the number of new policies written each day. New car sales reflect the health of automobile manufacturers and are of great interest to investors. But manufacturers release sales figures monthly — an eternity for investors. Since each sale requires a new insurance policy, the number of new policies issued each day provides a faster indicator. This becomes a profit stream for the issuers and for Quandl, which aggregates this data across the industry and packages it for investors.
  • Infomediate. Connect data providers and those who need the data. Here, the goal is not to provide content but to provide direction toward content. Google is, of course, the best-known example, but Quora, too, helps people find answers when expert help is needed. And there is huge opportunity here for others. In both their personal and professional lives, individuals spend hours each week looking for documents, reports, and other data. Find ways to connect these individuals with others who can provide the answers they’re looking for.
  • Exploit asymmetries. An asymmetry arises when one side of a transaction knows something that the other doesn’t. Exploiting this knowledge helps them drive a better deal. Hedge funds and used car dealers use such data to create and leverage asymmetries. More recently, sports venues, airlines, and others have begun using variable pricing to capture maximum revenue from consumers. All companies can examine sales and related data more deeply in search of such opportunities. Conversely, closing asymmetries, as Carfax does for used cars, can also present great opportunities.

Each of these seven options can help draw a specific use case to put your data to work, and in many cases a combination of these approaches can create incredible value

Use AI to your advantage

Once you understand the outcomes you’d like to get, you need to compare that with data available. The key is to find all the places where corporate data is being held, conduct a high-level inventory of the information that exists, then find use cases for which this data could be useful.  Of course you need to consider challenges such as legacy infrastructure. Most organisations use a mix of siloed and outdated point products that were built for one specific function, such as backups or file shares. Moreover, not all data is created equal. When analysing the data held, managers need to access its importance and protection requirements.

Success lies in being able to find the key insights from the massive amounts of data that businesses have today. This requires machine-learning–driven tools. Luckily, we live with times where AI and its subsets; Machine Learning and Natural Language Processing (NLP) tools can automate this in major part. Gone are the times where you had to rely on the rule-based Expert systems. Systems powered by the latest NLP like Untrite CoreAI allow the machines to learn from written description of use cases in human language. You no longer need to come up with a hard and definite set of rules to catch all corner cases and angles. Ultimately, data is most useful when it can help solve complex problems, enable proactive insights and provide real answers that add value to our lives.

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