Where Are You on the Data Analytics Maturity Model?
analytics maturity model

Use these 4 types of data analytics to access your small business’ analytics performance.



We have moved from an era where the businesses possesses of all the knowledge to today’s complex interconnected world where the buyer can acquire more information than the seller and thereby be in control. Thus, to remain competitive companies must incorporate the various social media dimensions and real-time analytics into their distribution, marketing and sales processes if they wish to maximize revenues and customer loyalty while minimizing costs and risks.

Smart visionary companies are well armed for the digital onslaughts. Some are able to change pricing periodically during the day to compete with online competitors that are doing the same. Others have know your customer (KYC), anti-money laundering (AML), credit risk, and/or fraud software as well as upsell features built into their systems so that they can maximize sales while minimizing losses.

This is the brave new world of engaging the customer in the physical and virtual environments simultaneously. It is one of the key ways companies are differentiating themselves.

1. Basic Analytics

To make sense of the vast volumes of data bombarding organizations, companies need systems of insight that can analyze the data.

The primary purposes of these systems are to drive revenues, improve loyalty, upsell, increase productivity, or minimize risk. Everyone does it; just some do it far better than others.

Some companies still do all their analytics on spreadsheets while others have moved from basic historical analyses to predictive analytics. According to some studies, organizations that embrace analytics are more than 2x more likely to outperform peers, grow revenues more rapidly, and increase profits more than 10x.

No matter how one slices it, businesses rely on analytics; but top performing businesses count on it to deliver enhanced results.

2. Historical Analytics

The basic form of analytics usage is historical analysis.

This can be a review of customer buying habits, sales by account type, inventory movements, value of a customer, and other baseline information that can help determine marketing programs, inventory management, sales quotas, etc. These basic metrics that business executives measure are very useful in looking at trends and planning. But they are just the baseline that all successful companies use.

While there are few differentiators in this mix, companies like Wal-Mart use the data as a competitive advantage for reordering and restocking shelves, modifying pricing, and moving inventory amongst and across distribution centers.

Less than 10 percent of all data is ever analyzed; thus, there are numerous ways executives can improve the business through more and better analytics.

The biggest challenges with historical data are its accuracy, consistency and currency. Data ages, which can almost guarantee that the older the data is, the more out-of-date errors there are in it as relates to today’s reality.

Consistency relates to the fact that an individual doing historical analysis will likely use multiple files and they may not all be snapshots taken at the same time, causing inconsistencies.

3. Real-time Analytics

More advanced companies add real-time analytics to the mix.

With an effective real-time analytics program that is integrated with online transaction processing, organizations can increase revenues, reduce risks, and be more responsive to customer needs. This approach enables companies to tailor in real-time the generic transaction-processing request into one specifically designed for the customer.

Amazon uses it to recommend upsell purchasing options based on one’s history.

Banks use it to determine credit risk or potential fraud and can take action while the customer is still online and before the deal closes. Police use it to determine if there are any outstanding issues or tickets with cars and/or individuals that they have pulled over.

Used effectively executives in most industries should be able to increase revenues (including collections) by up to 10 percent while shrinking their risk exposure.

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