The Art and Science of Business Intelligence

Data analytics is becoming a commodity that even startups and small businesses have access to. However, just because business intelligence…

Data analytics is becoming a commodity that even startups and small businesses have access to. However, just because business intelligence (BI) solutions are accessible, affordable, and can automate insight generation it doesn’t mean that by simply buying BI software, business executives can at once leverage its potential. You buy a solution, plug and play it, and presto! you’ve gained a competitive edge. In reality, BI doesn’t work this way.

BI is more than just the technology that underpins its potential. Woven into its core is the art of orchestrating big data to unison using old-school business skills and plenty of common sense. Rather than an add-on software to your IT infrastructure, BI is a management discipline that should be fully integrated into the business workflows and decision processes before you can fully appreciate its impact on the bottom line.

Here are three things to consider to get the best value out of BI.

1. Ask smart questions to surface insights

ETL and other analytical processes are mostly science: capturing, preparing, organizing, and reporting on data requires a systematic process and this is where BI software is most useful. BI tools makes data crunching easy and scalable, including faster query response, collaboration, and real-time insights. Finding the best business intelligence software that matches your business size and needs and instituting a BI policy across your operations are the first steps to get the science of BI covered.

Once you have the ‘science’ covered, you can move on to what , Lisa Morgan of Information Week defined as ‘art’ – asking questions about the data. Business Intelligence isn’t a race to collecting the most data or most specific details, but rather about understanding data. Data-driven Business executives must possess real curiosity and know that a single question is not enough to surface insights. A few important questions to ask are:

  • What is my goal for using BI?
  • What is the big picture?
  • What are the elements in this big picture?
  • What is the relevance of this data to my business objective?
  • Who in the company can I collaborate with on this data?
  • Do I see a pattern in data?
  • Am I not being biased to a subset that can obscure the overall integrity of the data?
  • Did I test the data before factoring it in my decision?

2. Know what you’re measuring, and why

The immediate concern when applying BI to your decision process is often what metrics to measure. Huge volumes of data presented in a myriad of ways can be overwhelming, and which gives rise to the issue of pinpointing the data to focus on – i.e.,, the ‘what’ of BI. For instance, a web analyst can choose to concentrate on sessions over page views or engagement level over reach, depending on the website being measured and its business goals.

However, you must understand WHY you’re tracking certain metrics and not others, and make sure this knowledge is trickled down to the line managers. Analytical targets can vary greatly, according to specific business objectives. For example, monitoring abandoned shopping cart rate can be a sales maintenance policy to ensure a steady month-to-month conversion flow or a means crisis intervention to troubleshoot why prospects are not converting at the checkout page. The data is the same, but different approaches can be taken to examining it, on account of differing rationales.

3. Combining external and internal data

One of the most powerful things you can do with business intelligence is to combine external data, derived from the market in which the business operates or other sources, with your own internal data. Data mashup of external and internal data gives you a more complete picture that either set alone cannot deliver. This may reveal new insights on product demand, niche markets, growth areas, and measure impacts of your business strategies that your internal data–e.g., financial and operations data–cannot reveal.

Sources of external data

  • Social data from LinkedIn, Twitter, Facebook
  • Competitor’s open data such as annual reports, press releases, web content
  • Government data including federal and state government datasets in Data.gov or other countries’ datasets in Open Data Showroom.
  • Published surveys, studies, and reports
  • Curated news including weather data
  • Use free third-party data mashup offered by vendors such as Data.com, Rapleaf, and CrunchBase
  • For more, check out this list of open data sources

Conclusion

Beyond the technical challenges of BI–crunching terabytes, capturing and surfacing data from multiple sources, servicing simultaneous multiple queries, etc.–is the art of leveraging BI to gain market competitiveness. Make sure to integrate the five points above at whatever part you are in the whole BI spectrum.

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