Data: Moving from Ordinary to Visionary


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Management information reporting is pretty standard now in all companies – after all, if the management team don’t know their key financial data points then there are probably more important concerns to focus on than how to integrate data analysis into other parts of the business.

Management information (MI) can span across the finance and operational departments, looking at staff KPIs, attendance records and revenue positions.  MI reporting can pull in data from the transaction processing systems, e-commerce platforms or financial applications – to give a view of the overall health of the business.

Business intelligence looks at taking this structured, often static information and making it more valuable and wider in its perspective.  Financial information can be combined with other data sets to provide insights that benefit not just the executive and financial teams, but also marketing and sales divisions to help drive their activities.

Moving from the ordinary to the visionary is about how to take management information and make it into business intelligence. 

 

How can information be continually made more valuable?

Business intelligence programs seek to amalgamate all of a business’ data into one system, in order to be able to cross-reference trends for deeper analysis.

Many tools now exist that can mine text across petabytes worth of documents to make data retrieval quick and easy – such as mining through emails to see how many times certain products were mentioned across a set period of time to determine potential product issues or popularity trends.

Text mining can be particularly helpful for support departments in reviewing and improving their operations.  Being able to mine the text of thousands of support calls enables management to better spot problems or identify breakdowns in support.  Once these issues can be identified (or a process for identifying issues has been formulated) then trend analysis can be utilised to spot ‘triggers’ to certain scenarios – such as an internet provider’s support calls may rise during bad weather at weekends when more people are surfing the net rather than braving the outdoors.  This could lead the internet provider to recruit more staff at weekends dependent on weather forecasts to reduce call waiting times when lines are particularly busy.

Business intelligence (BI) activities involve the collating of historical data, real-time data and future predictive analytics.  BI doesn’t just look at one set of static data, it combines data from different timeframes and scenarios to build a more accurate picture of the business through information.

For instance, a law firm we work with is looking at how they can combine all of their CRM data (which is relatively static on one level, but fluid in content at another level as new sales opportunities are inputted), with their prospect marketing database (such as e-mail distribution lists, e-mail tracking software data and event attendance).  Layered on top of this information will be data flowing in from their social media platforms, primarily LinkedIn and Twitter, in order to build a more holistic view of their business development activities.  Once this information is centralised, it is then easy to delve into trends such as uncovering the percentage of prospects that are converted to customers each month, what percentage of new customers come from their prospect list, and what percentage come from other sources.  Based on this information, the firm can then predict what revenue values mining this list of prospects will potentially yield in the future (i.e. anticipated monthly new customer revenues).

This data could then be of interest to not just the marketing division but also the finance department, showing how business intelligence can create data insights that are valuable across multiple departments.  This in turn encourages more sharing of data between departments, as the potential for new insights means better information output for each division.

Finance and marketing divisions sit on fairly opposite sides of the business.  However, combining CRM data with key financial information would enable the business to work out a number of key insights which could help them to better understand how they do business and what their customer base looks like.

Being able to understand the average revenue position per customer would help you to identify whether the business has too many customers with low levels of spend or in fact too few customers, each with a precariously high (and potentially business-damaging) average level of spend each.

CRM information combined with financial data can help companies to understand churn rates across their customer base, and what this in turn costs the business when customers leave.  It can also help to identify operational costs per sale for each customer – does one customer incur higher expense claims across your employees?  Or is the geographical location of a certain customer significantly reducing margin levels due to high travel costs?

Without business intelligence, this management information and fluid marketing data would be kept separate within the business.  The data would be there, but the value would be missing.

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