Tag Archives: analytical

Frustrated on Your Data Journey?

It’s going to take how much longer?! It’s going to cost how much more?!!

If those sound like all too familiar expressions of frustration, in relation to your data journey (projects), you’re in good company.

It seems most corporations these days struggle to make the progress they plan, with regards to building a single customer view (SCV), or providing the data needed by their analysts.

An article on MyCustomer.com, by Adrian Kingwell, cited a recent Experian survey that found 72% of businesses understood the advantages of an SCV, but only 16% had one in place. Following that, on CustomerThink.com, Adrian Swinscoe makes an interesting case for it being more time/cost-effective to build one directly from asking the customer.

That approach could work for some businesses (especially small and medium-sized busineses) and can be combined with visible data transparency, but it is much harder for large, established businesses to justify troubling the customer for data they should already have. So the challenge remains.

A recent survey on Customer Insight Leader suggests another reason for problems in “data project land.” In summary, you shared that:

  • 100% of you disagree or strongly disagree with the statement that you have a conceptual data model in place;
  • 50% of you disagreed (rest were undecided) with the statement that you have a logical data model in place;
  • Only 50% agreed (rest disagreed) with the statement that you have a physical data model in place.

These results did not surprise me, as they echo my experience of working in large corporations. Most appear to lack especially the conceptual, data models. Given the need to be flexible in implementation and respond to the data quality or data mapping issues that always arise on such projects, this is concerning. With so much focus on technology these days, I fear the importance of a model/plan/map has been lost. Without a technology independent view of the data entities, relationships and data items that a team needs to do their job, businesses will continue to be at the mercy of changing technology solutions.

Your later answers also point to a related problem that can plague customer insight analysts seeking to understand customer behavior:

  • All of you strongly disagreed with the statement that all three types of data models are updated when your business changes;
  • 100% of you also disagreed with the statement that you have effective meta data (e.g. up-to-date data dictionary) in place.

Without the work to keep models reflecting reality and meta data sources guiding users/analysts on the meaning of fields and which can be trusted, both can wither on the vine. Isn’t it short-sighted investment to spend perhaps millions of pounds on a technology solution but then balk at the cost of data specialists to manage these precious knowledge management elements?

Perhaps those of us speaking about insight, data science, big data, etc. also carry a responsibility. If it has always been true that data tends to be viewed as a boring topic compared with analytics, it is doubly true that we tend to avoid the topics of data management and data modeling. But voices need to cry out in the wilderness for these disciplines. Despite the ways Hadoop, NoSQL or other solutions can help overcome potential technology barriers — no one gets data solutions for their business “out of the box.” It takes hard work and diligent management to ensure data is used & understood effectively.

I hope, in a very small way, these survey results act as a bit of a wake up call. Over coming weeks I will be attending or speaking at various events. So, I’ll also reflect how I can speak out more effectively for this neglected but vital skill.

On that challenge of why businesses fail to build the SCVs they need, another cause has become apparent to me over the years. Too often, requirements are too ambitious in the first place. Over time working on both sides of the “IT fence,” it is common to hear expressed by analytical teams that they want all the data available (at least from feeds they can get). Without more effective prioritization of which data feeds, or specifically which variables within those feeds, are worth the effort – projects get bogged down in excessive data mapping work.

Have you seen the value of a “data labs” approach? Finding a way to enable your analysts to manually get hold of an example data extract, so they can try analyzing data and building models, can help massively. At least 80% of the time, they will find that only a few of the variable are actually useful in practice. This enables more pragmatic requirements and a leaner IT build which is much more likely to deliver (sometimes even within time & budget).

Here’s that article from Adrian Swinscoe, with links to Adrian Kingwell, too.

What’s your experience? If you recognize the results of this survey, how do you cope with the lack of data models or up-to-date meta data? Are you suffering data project lethargy as a result?

6 Tips on Recruiting Analytical Talent

The well-trailed difficulties in recruiting data scientists or other analytical roles, followed by the equivalent challenge in retaining them long enough to recoup your investment, have been likened to “talent wars.”

There are hotspots around the UK, but it seems all areas to some extent share this experience. London is perhaps the most challenging place to retain your talent. In my own experience, it has been easier to recruit in South Wales and Bristol (the latter being particularly good for having a pool of analytical talent), while much harder in Bournemouth and Edinburgh, for example. Several factors can improve your odds, including how you advertise, whether or not you use an agency and especially how clearly you explain the role.

Here are six tips:

Role description

Providing clarity on the role and what you expect from candidates is harder than it sounds in this sector. So many terms that you might use (like “analysis,” “insight,” “intelligence,” “data,” “modeling,” “reports,” “presentation,” etc) are open to interpretation, and some very poorly skilled candidates use this language to describe what they can do. For this reason, I recommend avoiding technical jargon as much as possible (apart from specifying any exact software in which you require expertise). Seek to describe the role in terms of the outputs you require the person to be capable of delivering. For example, do you want a candidate who can produce analytical reports or someone who can influence marketing leaders and present information that is sufficiently persuasive to change strategy or guide design of a new campaign or product.

Advertising and Agencies

Advertising your role is another conundrum for the would-be hiring manager. Given the high fees charged by some recruitment agencies, for little visible effort, it’s not surprising to see the growth of companies investing in their own recruitment portals and greater use of LinkedIn by recruiting managers. The latter approach has the advantage, for well-connected professionals, of both tapping into their existing networks and approaching those who both understand the language they use and may be best placed to know analysts ready for a move. However, the novelty factor has now worn off, and with so many recruitment consultants also bombarding LinkedIn users it is harder and harder to get your message across.

I would certainly encourage use of your own company advertising (to tap into fans of your brand) and LinkedIn as a first step. However, despite all the charlatans in the industry, I have still seen real benefit from specialist agencies that genuinely know this market. Having recruited analysts for more than a decade now, I’ve found these informed specialist recruitment agencies few and far between and those I trust to be even rarer. However, among this rare breed, I am happy to recommend MBN recruitment. The firm always understood my brief and provided viable appropriate candidates as well as pragmatic advice on salary and approach to wooing the undecided.

Motivating and Retaining

As all insight leaders will be only too well aware, even though finding the right analytical talent in the first place is challenging, it can be even harder to keep them motivated, engaged and ultimately retain them long enough to see their potential realized and value added to the business. Every journey starts with a single step, as the Chinese proverb goes, and it is really important to start well. For anyone who has not yet read it, taking the approach recommended in “The First 90 Days” can be a recipe for any new hire (especially at a more senior level) to hit the ground running and make the right first impression.

On-Boarding Coaching

I’m also conscious that leaders of insight teams are even harder to find, so many organizations are needing to appoint, to the growing number of these roles, candidates with strong generic competencies but little or no experience of customer insight. Coaching at Work magazine recently published an article on on-boarding coaching and its growing popularity. Laughlin Consultancy can see a need for trained executive coaches with a background in customer insight leadership to help support this population to be as effective as possible through their first 90 days and so are providing that service.

Performance Management

Continuing motivation and engagement of analysts could be a blog post topic (if not a book) in its own right, but for now suffice to say that there is a natural tendency for this population to be more cynical. Marshall Goldsmith described most performance management systems as an occupational hazard at best, and there is a need to flex the company policy to better work for these skilled people. I was struck when reading “Punished by Rewards” as to the importance of not relying on bonuses or internal recognition systems to bribe them to work hard or give a high score in the next engagement survey – rather being genuinely interested in the work that they do and reclaiming the essential importance and nobility of that craft. For performance reviews, I would also recommend taking the approach recommended by Nancy Kline.

Competencies and Career Paths

One final recommendation, to achieve motivated and retained capable analysts, is to invest in a clear career path for them. People, especially analytical people, want to understand clearly how their skills match up to the ideals for each role and potential routes for their development if they can improve and “up-skill.” I have seen skilled analysts become very motivated by simply having clearly documented competencies for different technical roles and seniority within them. When you add to this clarity as to potential career routes through that matrix, it can lead to conversations and planning that result in those analysts staying for many years not just months.

I hope those tips are helpful to you. Please do share what has worked for you, too.