5 Reasons to Invest in Data and Analytics in 2016

It’s no secret that the amount of data in the world is growing at a tremendous rate. Some anticipate that the world’s data volume will grow 40% per year and expand to fifty times its current volume by 2020. Think about your own organization: how much data did you have ten years ago? Five years ago? Even just two years ago? Without a doubt, it is much less than the current amount of data.

What can we do with all that data? The terms “big data” are used more and more, but how can we turn that buzz word into something actionable.

To kick off 2016, below we describe five reasons to invest in data and analytics this year.


1. Data is Growing – and Becoming More Disparate

We already mentioned that data is growing at a fast rate. This is true within your own organization. With each new application deployed or upgraded, it tends to capture more and more data.

Here’s the catch: if you are not attempting to centralize and integrate data from various sources, then your data is only becoming more disparate.

As data grows, so too does its isolation from other sources. How many opportunities are you losing to up-sell, cross-sell, more accurately and efficiently market, or provide better customer service and experiences because data is not integrated to support this type of analysis? How much do these lost opportunities cost your organization every year?

Let 2016 be the year you decide to integrate your data from various applications.

2. Bad Data Quality is Costing Your Business…A Lot

As part of any business intelligence, data, and analytics implementation, your organization must look at the quality of your data. Are their inaccuracies? Discrepancies? Confusing definitions? A successful data and analytics practice ensures that data is accurate and key definitions are well-defined.

The Test: Ask staff from several different departments how many members/customers your organization has. How many different answers do you get? Who is correct?

To learn how important data quality was to a business, one study found that the cost of bad data was equal to between 10% and 25% of the organization’s revenue.

Make 2016 the year that data quality no longer costs your business.

3. Your Competition is Investing in Data and Analytics

A recent Gartner survey showed that 75% of companies are investing or planning on investing in big data in the next two years. Forbes reported that nearly 83% of organizations are prioritizing data initiatives as of critical or high importance.

As your business’ competitors invest in data and become more data-driven, how will your organization respond? Soon, data and analytics will become not a competitive advantage but a competitive necessity.

Don’t let 2016 be the year you fall behind the competition.

4. Your Organization is Inefficient at Reporting Information

Executives far removed from day-to-day operations tend to be surprised by this fact: quite a bit of your organization’s reports are being built manually. This takes time. A lot of time. Manual report building tends to be completed in Excel opening up significant opportunities for errors in formulas or other fat-fingered mistakes (see the cost of bad data above).

If analysts are spending 40% of their time manipulating data and creating reports, how much time are they actually spending analyzing that data and those reports? These reporting inefficiencies can cost your organization a minimum of tens of thousands of dollars a year. Any data and analytics strategy should include an emphasis on automating reports and minimizing the amount of manual, time-consuming data manipulation.

Will 2016 be the year your staff no longer spend thousands of hours a year building reports that could be automated?

5. Data is the Best Tool for Process Improvement and Optimization

Many tend to equate data and analytics with data warehousing, data visualization, and predictive models. Most don’t realize that investing in data and analytics can identify ways to improve and optimize key business processes.

A bank or credit union’s consumer loan origination process is a perfect example. The consumer completes an application – usually online – and that application goes through several processes. Underwriters, processors, and funders are all touching the application as it moves through the application pipeline. Can you track when those handoff points occurred? Do you know where consumer’s are abandoning applications and why?

If you were able to capture all of this information, imagine how it could help improve the lending process.

Understanding and resolving consumer pain points will improve customer satisfaction and experience. Understanding handoffs and touch points throughout the application process by staff will enable better tracking and communication to reduce the application timeline.

Use data and analytics in 2016 to understand, diagnose, and optimize key business processes


The Knowlton Group can help you take the next steps in developing your organization’s data and analytics program. Contact Brewster Knowlton at brewster@knowlton-group.com or call 860-593-7842 to start the conversation today!

Posted in DW/BI.

6 Comments

  1. Brilliant article for those who really understand the issue (mostly sellers). The big questions is how do you convince those who are responsible to solve these issues?

    In most cases business growth is compensating for the cost of bad data and is not a major concern for many large organizations.

    I would like to understand if there are any counterviews on this.

    • Ravi,

      Each business is going to have its own strengths and weaknesses when it comes to data and its utilization. Inefficient organizations might benefit from process optimization and reporting improvements while organizations that have poor data quality and trouble getting accurate insights might focus on that aspect. For example, if our customer’s address information is full of issues, any market share analysis – perhaps by county or city – will be minimally accurate at best.

      I would disagree that business growth is compensating for the cost of bad data. It might be hiding the cost of bad data but that is by no means a compensation. If an organization improves 7% YOY, how does that company know it shouldn’t have had a 15% YOY growth with improved data quality? In that scenario, the problem is hidden but most certainly still exists.

      Each company needs to take an honest assessment of their operations and their data and analytics programs. Once that discovery is complete, then the diagnosis process can begin which would include an estimation of the return an improved data and analytics program could yield.

      -Brewster Knowlton

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