A Comprehensive Guide to Overcoming the Most Common Data Integration Challenges

Insight development is based on gathering information, then data integration and analysis. However, organisations often find this challenging due to multiple sources, formats and time scales. Do you?

Many companies struggle to benefit from all their data and information because they don’t know how to turn it into insight, or their insights remain interesting but not actionable. There are many reasons for this.

From data quality issues to technological limitations and resistance to change, organisations must navigate a complex landscape to unlock the full potential of their data.

This comprehensive guide delves into the ten most common challenges in insight development, offering detailed analysis and strategies to overcome each obstacle, ensuring your organization can harness data for strategic advantage.

If you prefer to listen rather than read:

 

What an Actionable Insight Really is

I get so frustrated when people refer to numbers, data, or the findings from research projects as insights. None of these are!

In addition, developing actionable insights from a single survey is rare.

The reason is that insight development, getting to that “aha” moment that everyone immediately understands and wonders why no one thought of before, needs a 360 perspective of the challenge or opportunity under investigation and uses information from multiple sources.

There are many definitions of insight, but the one that I use, and that resonates with my clients, is a statement that impacts the attitudes or behaviours of current or potential customers/shoppers of a brand or category based on a human truth that results in an emotional response.

At first glance, this may seem like quite a mouthful, so to simplify retention, I refer to it as ABCDE:

A = Attitudes and Actions

B = Brand or Category

C = Customer, consumer, client or shopper

D = Deep human truth

E = Emotional response

To fast-track your understanding, here are some great examples of the insights behind some of the best-known brands:

  • Heineken Jillz: I want to drink alcohol on a night out, but I don’t like beer and wine is too variable in quality.
  • Kraft Philadelphia: Food is delicious, but I don’t want to eat too much fat (butter versus cream cheese).
  • DTC Diamonds: I want to stand out (shine), but as a modern woman, I also want to be seen as gentle and feminine.
  • Unilever Dove: I want to be admired for my beauty on the inside, not for what I look like on the outside.
  • AXE (Lynx in UK): I (young men) want to attract as many beautiful and sexy women as possible.
  • Haribo Starmix: There’s a child inside every adult.
  • Dulux sample paint pots: I love to decorate my home, but I don’t want to look stupid by choosing the wrong colour.

You’ll notice that most are written in the first person as if the target audience is speaking. This makes it much easier to understand and resonate with the reader without much effort … Click to continue reading

Market Research, Business Intelligence & Big Data: Have we Forgotten about Human Data?

The annual pilgrimage to the ESOMAR Conference took place last week in Dublin. I heard that there was much discussion, both on and off the stage, about Big Data and the future of market research. Hopefully, the whole profession will get behind one initiative, instead of each individually trying to “solve world peace” on their own!

This week sees the second Swiss BI-Day taking place in Geneva and there will no doubt be similar discussions about Big Data and the future of Business Intelligence.

It appears that Big Data is not just a buzzword or a commodity that has been likened to oil; it has become the centre of a power struggle between different industries. Many professionals seem to be vying for the right to call themselves “THE Big Data experts”.

This got me thinking about the future of data analysis in general and the business usage of Big Data more specifically. There seems to be no stopping the inflow of information into organisations these days, whether gathered through market research, which is proportionally becoming smaller by the day, or from the smartphones, wearables and RFID chips, that get added to every conceivable article, more generally referred to as the IoT (Internet of Things). Who will, and how are we to better manage it all? That is the question that needs answering – soon! (>>Tweet this<<)

Data Science Central published an interesting article earlier this year called “The Awesome Ways Big Data Is Used Today To Change Our World”. Already being a few months old probably makes it a little out-of-date, in this fast changing world we live in, but I think it still makes fascinating reading. It summarises ten ways that data is being used:

  1. Underst anding and Targeting Customers
  2. Underst anding and Optimizing Business Processes
  3. Personal Quantification and Performance Optimization
  4. Improving Healthcare and Public Health
  5. Improving Sports Performance
  6. Improving Science and Research
  7. Optimizing Machine and Device Performance
  8. Improving Security and Law Enforcement
  9. Improving and Optimizing Cities and Countries
  10. Financial Trading

Many of these are not new in terms of data usage nor business analysis. What is new, is that the data analysis is mostly becoming automated and in real-time. In addition, the first and second items, which were largely the domains of market research and business intelligence, are now moving more into the h ands of IT and the data scientists. Is this a good or bad thing?

Another article posted on Data Informed a few months after the above one, talks about The 5 Scariest Ways Big Data is Used Today   and succinctly summarises some of the dynamic uses of data today. The author of both pieces, Bernard Marr, wrote that “This isn’t all the stuff of science fiction or futurism. Because the technology for big data is advancing so rapidly, rules, regulations, and best practices can’t keep up.” He gives five examples of where data analysis raises certain ethical questions:

  1. Predictive policing. In February 2014, the Chicago Police Department
Click to continue reading

Latest Post

divider

[pt_view id=”999bb999ha”]

Join Global Customer First Strategists!

Get our latest posts before everyone else, and exclusive content just for you.

* indicates required