Trending now! Integrating Business Intelligence Data with Market Intelligence Insights

June 16, 2014. The increasing automation of customer service processes and the use of centralized cloud database warehouses have opened up new possibilities in the analysis of consumer behavior, service usage and other performance data. Technological advances today also make it possible to analyze enormous volumes of data with unprecedented scalability and speed, enhancing the possibilities for business intelligence. More and more, business managers are now asking, "How can we integrate our business intelligence with market intelligence in order to provide more complete and comprehensive input for our decision making?".

What is business intelligence?

Business intelligence (BI) is the overarching term that describes the collection and analysis of an organization’s own data, including sales data plus legacy documents (archives of statements, insurance forms, medical records and customer correspondences). This intelligence is typically used to control costs, understand operations and performance, as well as increase productivity and effectiveness.
BI answers the question of whether a company has all the necessary resources and processes to operate successfully in a particular market. If it does have the right ingredients, then BI can also help shed light on how effectively and efficiently the resources are being used.

How does it compare with market intelligence?

Just like business Intelligence, market intelligence (MI) helps to improve decision making. The difference is that BI focuses on a company’s own data and MI focuses on external information. Compare this to driving your car: the dashboard provides your business intelligence while the view from your windshield is your market intelligence.

Market intelligence gives you a clear picture of market opportunities, threats, customer priorities and the competitive landscape. Analyzing all this information will help you decide on how you can grow the business, gain market share, launch new products or enter new markets.

A combination of BI and MI can show you whether your internal resources are optimally aligned with external market potential.

Let’s look at an illustration. MI reveals that a company has a low market share in a high-growth market (A) and dominates a slow-growth market (B). But it is through BI, that the company finds that its sales and marketing resources are too high in market (B) and should shift its resources to the market (A).

The challenge of integrating MI and BI

Utilizing both MI and BI can provide very powerful insights ““ but the challenge is that business intelligence and market intelligence data come in different structures and formats. This affects how the available data is accessed, combined, analyzed and used.

Business Intelligence mainly comprises numerical figures in dedicated internal data warehouses, which makes it easy to store, retrieve and analyze. BI often comes in well structured, standardized and readable formats.

Market Intelligence is much more complex, comprising both quantitative and qualitative data (verbatim) in separate storage systems. With the advent of social media, we can also collect MI in the form of pictures, movies, gestures, emotions and so on. This requires completely different ways to collect and analyze data.

All these mirror the challenges with Big Data, meaning that we often find our data sets are too large and complex to manipulate or investigate with standard methods or tools.

Big Data, big challenge

Big Data is currently estimated to be over 2 zetabytes and it is growing rapidly. No one can really comprehend how big 1 zetabyte is, because it is too enormous!

One thing is for sure – organizations do need people, processes and systems to turn MI and BI “big data” into useful information, intelligence, insights and informed actions.

The eight challenges for Big Data are:

  • How do we capture it all?
  • How can we curate it?
  • How should we store it?
  • How can we make it searchable?
  • How can we make it readily share-able?
  • How do we transfer it?
  • How can we analyze it?
  • How can we visualize the information?

These challenges also apply when we integrate MI and BI data for analysis.

For a market intelligence manager specifically, the main challenge is how to access and understand all their internal BI and external MI data so that as to convert it into intelligible input for decision making.

How to integrate BI and MI for business impact

1. Start by standardizing market intelligence data

BI data is usually manageable, it is MI that is more complex. So start by simplifying, ordering and unifying the different formats of MI in a way that will allow you to conduct more sophisticated analysis later on.

2. Consider bringing on social intelligence expertise

Social intelligence is about pursuing social media data that is relevant for analysis and decision making.This requires new skills in order to manage and engage an online community of trend spotters, reach out to novel sources of expertise, understand network-mapping and influence-rating metrics, assess the expertise and determine the relevance of community members etc..

MI professionals need to be able to have team members who are able to decipher and analyze things such as buzz volume, qualitative insights, consumer sentiments, weak-signals, etc.

3. Capture insights from visuals and videos with text or numbers

Analyze and transcribe the content of multimedia videos and images that are most relevant to your business. Look out for frequency and trends.

4. Conduct a qualitative-quantitative conversion

Next, pick the most relevant qualitative intelligence (verbatim) from MI and convert that into quantitative data as much as possible. Do this for all your current and future MI projects.

5. Structure for MI-BI cohesiveness

When all this is done, it is easier to understand how the MI obtained needs to be structured In order to make it suitable for further analysis with BI.

6. Find a way to access both MI and BI data faster

Both MI and BI provide valuable information, but only if we can access them in a timely manner. Review your data handling processes to ensure you can handle both well.

7. Optimize the Extract, Transform and Load (ETL) process

Make sure that the right data is easily extracted and transformed accurately, in terms of relevance, quality, accuracy, reliability, completeness and recency.

These principles will help any company plan their steps towards integrating their BI and MI data. The results can lead to millions of dollars in new revenue, as seen by our clients. So while the process is tedious, a well planned execution can reap great benefits.


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