Paul Laughlin

Although Edward de Bono's Six Thinking Hats technique has fallen out of use, it's worth another look at what can be a powerful tool for creativity.

Once you start to work for prolonged hours at home, you notice how different your environment is to the office.

As I was reminded recently, there are three aspects of leadership that can benefit from more intentional pauses.

Too often, analysis ignores commercial priorities or constraints for the business, and recommendations might actually destroy value.

Some data science teams have matured beyond offering advice and are making products. Do they need a data science product manager?

Do not start with fashionable technology trends or the most passionate speaker at that conference. What do your customers want?

Blockchain could finally start to matter to data leaders for analytics. But more likely it will be 2020 before we see serious use.

Analysts can be naive about office politics, so here is a simple framework to bear in mind when interacting with stakeholders.

One might think AI is risk-free, even though so much that has gone wrong already should cause us to be circumspect.

A few, important KPIs can be enough to model the most important business processes but must be anchored in the organization.

GDPR may have (just) been about data, and its protection. But it teaches us all to be visionary, agile, adaptive, trustworthy and pragmatic.

A "competency framework" can help analytics, data science or customer insight leaders in a wide variety of ways.

Once your analysts have a clear business question to answer, do they start new analysis each time, potentially reinventing the wheel?

Despite the advent of deep learning and other advanced techniques, most data science teams still struggle with more basic data problems.

What story will make the status quo so unacceptable that organizations must make the leap to blockchain?

The current approach to blockchain could mean a continued risk of siloed thinking rather than the needed cooperation.

With regulators emphasizing behavioral economics, the days of assuming customers will act rationally are numbered. Seller, beware.

Many firms aspire to "customer-centricity." Far fewer achieve both improved customer experiences and sustainable profit growth.

What may seem to be simply a clarification of language to a compliance expert may have major implications for company data models and storage.

There is a sense of complacency about GDPR, but the importance and the time needed to adapt mean that leaders must focus now.

Fine words about protecting privacy don't mask how consumers are being denied decision-making power about their own data.

Marketing automation systems show a lack of integration of customer insight. This is such a missed opportunity.

There has been quite some debate within the coaching community about the need to improve methods of measuring effectiveness.

Customer insight work is, by nature, exploratory and innovative, and it requires a flexibility and level of risk that run counter to IT processes.

Staffing has more options than in years past, with more data, analytics and research agencies, consultancies and contractors.

Metadata does not just represent an arduous maintenance task; it can be a gold mine of opportunity and time-saving.

The selection of coaches often still lacks a robust, structured process; here is a three-stage approach that can help.

Generating (and acting on) customer insight requires skills in prioritization, in getting buy-in and in presenting results.

Even with the emphasis on analytics and big data, companies are totally unaware of the powerful opportunities with machine learning.

You may think it's IT's job to avoid falling for attractiveness, but soon the majority of IT spending will be the responsibility of others.

Berliners showed me that, just when I thought I had them figured out, I hadn't. Customers should be treated with the same care.

Once dismissed as a fad, leadership coaching has been embraced, and the academic evidence for its efficacy has surged.

Marketing leaders share insights on the need for personalization, emotions, the humanizing of insurance -- and more.

Most corporations struggle to get to their desired destination on data -- because they've lost track of the need to start with a map.

Ever since Google made “data scientist” the sexy job title for the decade, suppliers and users have obsessed over analysts' technical skills.

It's getting harder to find a large enough, representative sample for consumer research. Gamification may be the answer.

Customer insight models are dated. They should focus more on events in customers' lives, and less on "needs-based segmentations."

Too often, data analysis is an unplanned art, with too many "rabbit warrens" being explored. A disciplined approach is required.

Several factors can improve your odds of recruiting and retaining the right people -- especially, how clearly you explain the role.

Far too often, what internal customers request from Customer Insight isn't what they really want or need. Socrates can help.

Public segmentations can lead to insights into groups like Millennials but can lead to overgeneralizations and can be manipulated.

To see how effective you are, you need a combination of technical skills and must measure "above the line" and "below the line."

While strategies supposedly begin with customers' desires, they mostly don't. A real customer-insight strategy needs to begin with three steps.

Developing a culture of action for customer insight teams requires saying no to many requests for help and driving toward recommendations.

To start with, DON'T try to raise your own agenda topics, based on current work from the customer insight team. Fit into their agenda.

The author accessed Indian analysts at a fraction of UK wages but ran into problems and eventually ended the analytics pilot.

Centralizing the capabilities devoted to customer insight can produce efficiency but also carries four important risks that must be addressed.

While there are wars fought over the three most common metrics, it turns out that the metric isn't what matters.

The field of behavioral economics describes 10 irrational biases, but too few insurers are taking advantage of the knowledge.

To test and learn, you may need to be a data scientist, psychologist, artist, storyteller, sales coach, economist and leader. Quite the challenge.