One of the primary characteristics of modern business is data – so much data that a lot of it isn’t even useful. And rather than focusing on customer data that can boost retention rates and simplify sales, many organizations are extracting data from every interaction, resulting in a flood of information.
How can you restore the original power of big data?
Simply put, it’s time to step back and assess what data you’re collecting and why. By developing clear objectives and pairing them with actionable data classes, you can propel your business to the top.
If you’re trying to understand why big data is struggling to keep up with business needs, the insurance industry can provide several key insights. In the insurance industry, companies have been collecting data on every interaction for years, and unsurprisingly, big data hiring is at its peak. That includes an emphasis on data management.
Data management is a broad category that includes warehousing, eliminating data silos, and – perhaps most importantly – data cleansing and quality assurance.
Data management professionals are the individuals within your organization who insure that you aren’t relying on out of date information about past customers, that your files aren’t full of duplications, and who can smoothly integrate multiple data channels. Collection without management sets your business up to fail.
Clean Up Your Data
At noted, data managers have many responsibilities, including data cleansing. But what exactly is being cleansed?
Some people hear the term and assume that it means eliminating data you don’t “like” – that it’s selective analysis. But data analysis is a formal process. When you delete a duplicate entry in your phone, for example, that’s data cleansing. Similarly, when you go through your computer files and delete an out-of-date file, that’s also data cleansing.
Professional data cleansing works in a similar way, to eliminate duplicate entries, delete inactive clients, and integrate legacy systems and new data storage programs. Most organizations have cluttered data systems, packed with old client information and inactive profiles. This old information is preventing your business from drawing actionable, accurate conclusions about your customer base.
Analyze and Act
One of the strengths of data management is that it can help your company differentiate between actionable data and excess.
The customer intelligence experts at BuyerGenomics have a succinct explanation: “if it’s not actionable, it’s not intelligence.”
Intelligence, in this definition, is only that which can be synthesized for meaningful application. It’s a sensible approach to excessive data. In fact, this definition makes it clear that data you can’t use isn’t really data at all.
To make this approach to big data work, you obviously need clear, specific goals. “Increase retention” or “make more money” aren’t actionable goals. Every organization wants these things.
Actionable goals are data-driven, attached to particular data points. By defining measurable key performance indicators (KPIs), such as increasing sales by X amount in a given region or reducing the customer acquisition costs, you make it more likely that data can help you meet your goals.
Return To Retention
Many organizations are currently emphasizing retention over customer acquisition due to increasing acquisition costs. So how does big data apply to this particular problem? Well-managed data may be at its most powerful when applied to issues of retention because your data already reflects the target audience. The main challenge facing businesses, then, is breaking down data silos to increase a company’s flexibility.
Eliminating data silos helps your business work creatively and collaboratively, emphasizing coordination across departments and a multi-disciplinary approach to data. When specialists from different departments come together over a shared goal, in this case increasing customer retention, new solutions are quick to emerge. Data can be isolated in silos, but data professionals can experience similar isolation.
It’s time to toss legacy systems that are flooding your data banks with obsolete information. Advanced data management can transition this information to a modern, analytics driven system and clear out the garbage. Clean data fuels reliable predictive analytics, but legacy systems reinforce silos.
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