Do You Have a Love-Hate Relationship With Your Data?

February 19, 2015
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Can’t live with it? Can’t live without it?

Has your relationship with data seen better days?

When the business is ticking over nicely, and data is fresh, everyone’s content and happy. They can get on with their work, free from the burden of decrepit databases and phone numbers that never connect.

love-hate-data-twitter

Can’t live with it? Can’t live without it?

Has your relationship with data seen better days?

When the business is ticking over nicely, and data is fresh, everyone’s content and happy. They can get on with their work, free from the burden of decrepit databases and phone numbers that never connect.

But then the honeymoon period ends, and the relationship needs work. It’s getting harder to email people. You’re always getting names wrong. And half of your mail comes back as returned to sender.

Like Elvis’ love letter, your mail need not be sent back with an ‘address unknown’. Just as Valentine’s Day gave us all good excuse to nurture relationships and ensure their longevity, it gives us the opportunity to reflect on the way we relate to data, and whether we spend enough time on making sure it’s working for us.

Invest in the Best

Last weekend, the world spent around $13 billion on Valentine’s Day. We shopped for 180 million cards, bought 196 million roses and spent $2.2 billion on jewellery.

Over a year, businesses spend a fraction of that amount on data quality initiatives – a ‘mere’ $994 million in 2012, according to The Information Difference.

So we clearly spend far more nurturing our partners than we do nurturing the best asset our business could ever acquire. In context, it’s clear that both require investment if they are to survive. While Hallmark Cards have a healthy future ahead, we need to spend more on our much-loved and much-valued data, and invest more time in measuring genuine return on investment.

Love Data… in the Real World

Dating site OKCupid uses sophisticated data matching in order to pair up potential lovebirds. Its algorithms are responsible for sorting through more than 70 terabytes of data about the people in its database. Their likes, dislikes, personality quirks and ideal matches all paint a unique portrait of what they’re looking for in life – and in love.

Naturally, people change. OKCupid has to continue to keep people engaged in enriching their own data and updating it frequently – or until the right match comes along. In essence, it’s an exercise in big data management. And just as errors are introduced unwittingly into big data, so dating sites have to cope with people’s idiosyncrasies. They have to filter out the noise and use data to define the person you really are.

If you’re in any doubt about the value of accurate data, look at OKCupid. It’s data is worth millions. The company was purchased four years ago for $50 million, primarily because it uses data to gain unbeatable insight into its users, and it understands how to purify and segment its data to ensure the matches it achieves are second to none.

When is Dirty Data Good Enough?

Some opponents will argue that data is “good enough” in its current state. They will argue that data quality is too much of an expense, and unless your business is also worth $50 million, there are few gains to be had by correcting it. These colleagues have certainly fallen out of love with data; they are so used to developing workarounds that they will argue the case against change.

Granted, there are some – very limited – scenarios where data need not be timely and precise. For example, when we touch on big data, we immediately think about data sets filled with noise, junk, error and anomaly. It would be foolish to presume that all big data could be cleaned and honed precisely, since big data generation never stands still for a second.

Many of us are so used to tolerating imperfect data that we daren’t imagine a world where data is clean and accurate. We fell out of love with our data, and learned to live with its growing flaws. We accepted the fact that our data would tell us a white lie now and then. But remember the simple saying: “garbage in, garbage out”. Eventually, bad data will come back to haunt you – a skeleton in the closet that never quite disappears.

Rescuing the Relationship

Remember the golden rule of customer retention? It’s much cheaper to sell to an existing client, rather than trying to market to a new one.

But hear this. If your data is ageing, inaccurate and unreliable, take that saying with a large pinch of salt.

Most businesses understand that investments in data quality will pay dividends. They fail to appreciate the rising cost of inaction. Measuring this is difficult, but Econsultancy spells out the potential consequences and pitfalls very nicely in its 2014 poll:

  • Poor data from the website means no action can be taken to follow up
  • Poor face to face data capture injects errors into a live database
  • Lack of error correction lets bad data linger unchecked
  • Bad data renders loyalty programs useless
  • Mis-addressed mail results in waste
  • Manual checks are completely inefficient in dealing with data problems

If your data isn’t loved and cared for, it will eventually wither away. But you and data are not done yet. You can rescue your ailing relationship by giving your data some attention now. And improving the way you acquire new data will help you avoid hiccups as your data matures.

A little tender loving care will future-proof existing data and help you build insights and knowledge that will fuel the business and drive growth for years to come.