Tag Archives: data elicitation

Data Strategy, high time to take action!

Way too many companies are still behind schedule, and have the utmost difficulties to set up such data strategies, especially in France. I have then decided to rely on my most significant successes from the past year to review and complete the operational scope of Data Elicitation.

The achievements :

  • The CWA, which makes me the only French data management expert who also is certified in digital analytics;
  • The success of the first Paris MeasureCamp, which I have been co-organizing, the biggest analytics event in France, which is due to happen again on June 27th 2015;
  • The patent validation process going on now at a European level, which validates even more my data management expertise;
  • Les multiples requests I have been addressing, from web analytics to strategic consulting, including text mining, data visualization and big data, many topics for many original inputs.

The restraints :

Still, working on data and digital policies has proved rather difficult; in fact, the restraints in implementing these strategies are clearly more structural than cyclical; McKinsey is summarizing the French situation in 4 items, in this study (only available in French…) published in the fall of 2014 :

  1. organisational issues, and namely the all-too famous vertical organization that I have reported here
  2. a lack of digital competencies
  3. a lack of financial leeway
  4. a lack of clear managerial involvement

The French State could certainly act more and better on two of these issues, education and business taxation. I shall develop more in detail the opportunities for public policies in the digital area in a future blog post.

The two other restraints that McKinsey have identified are more complex, as they are linked to the internal organization of the companies, as well as to their willingness to change.

In my eyes, the French companies have to overcome three biases, which harm their blooming in this data-driven world :

sujet négligeable

  1. Data (and digital) are very often second-class topics, which are handled after sales and financial issues of any kind, when there is time, so to say very rarely as a priority. The website? A necessary evil. Social networks? we have to be able to reach young people! Data management? Sure, we have a CRM. So many prejudicial and sweeping statements: data and digital are downgraded as cost centers, and absolutely ignored as growth drivers.désaccord dans le groupe
  2. Investing into a data strategy is often subject to collective decision, through a board or a project coordination, and seldom the will of a single person. Hence, as for most “collective” decisions, it often is the lowest bidder who wins, the most careful, the conservative. On top of this, the competition between various department, be they marketing, IT, finance or sales, generates a paralysis, where emulation would be required.information sous clef
    1. Finally, and that is a key subject, the various data owners still consider that exclusive information ownership grants them an additional share of power. What a mistake! at the very moment when an information is stored, it is losing all its value, as data only have a meaning as they are enriched by others and used for decision-making.

An example? Three departments, marketing, sales, finance. Three products, A, B and C. Marketing has done some research, and clearly A is the best product. Sales are positive, B is the best-seller. Finance has analyzed the ROI, and C definitely is the most profitable. So two options: the wild-goose chase, and then the quickest, or the most convincing one wins, or one share information in a transverse way, so as to ponder the best mix for the company. One certainly would wish the second option happened more often…

Wherever there are data, there should be first an analysis, then a decision process and eventually an assessment.

The outlooks :

These blocking points have led me to rethink what Data Elicitation core business should be in the short-term.

As a matter of fact, it is vain to try to convince some companies to work on their global data strategy, when they still are burdened by the restraints as depicted above, while they have not realized yet how large their potential could be. therefore, I have created some training modules, so as to make the concerned professionals aware of the necessity to think their data management in a transverse and global way.

You will then find on this website, under the header “training“, a description of modules dedicated to people training, into such topics as data management and analytics, both on the methodological level and through such concrete actions as database maintenance, data sourcing or quality assurance.

Or course, I shall keep on consulting at C-level and Executive levels, when they are willing to handle their most acute data strategy issues.

You know it all, now… Your comments and/or questions about those modules are highly welcome, as well as any suggested improvement.

Now, there only is one thing to do, e.g. share this blog post IMMODERATELY…

I hope to hear from you soon!

[cette note de blog en Français est ici]

Data Elicitation in three steps (1/3): Data Patterning

In my previous post, I have outlined what Data Elicitation was about. I have introduced the three areas required for a proper eliciting process, e.g. Data Patterning, Data Enrichment, and Data Analytics. This post will deal with the first of these areas, “patterning“.

First, it is worth explaining why I chose this word. I have used a typical concept from the textile industry (or, to be a bit more ambitious from the “Haute Couture” world). In this area, a pattern is the intermediate stage between the designer’s sketches and the item production, a formalized plan enabling industrial planning. On one hand, it still is a concept, like the sketches, as it is purely paper. But on the other hand, it already is production, as it includes all the necessary information for implementing a full production process. This is what patterning is all about, allowing people’s ideas to become physical shapes.

Patterning data is an essential step for data management,as it allows to take stakeholder wishes and technical constraints into account, and prepare an optimal project and development planning.. No database is suitable, if not driven by clients’ needs and requests. No data analysis is relevant if not aligned with the previously agreed pattern.

A few key questions in this respect:

  • What are my data made of and, even more important, made for? → as one finds its way better when the ultimate goal is known…
  • How can data sets be best organized? → the proper content ought to be in the proper place
  • What content do I need to store to get the best out of my data? → since not every piece of information may be worth keeping
  • How may my data relations be best optimized? → data are more useful when they are properly linked and aligned

I have summarized a typical process in the table below, in three columns; a fashion analogy, some project management steps and a basic (softened) example inspired by one of my previous experiences in the mobile world:

Patterning steps

The parallel with the creative flow in the Fashion industry is strong, as it shows that the first half of the process (steps #1 to #4) is the true added-value to the whole content, the second half being more execution. It is clear that botching the patterning phase will impede the proper completion of the project. In the case above, the proposed solution could be summarized in a small chart, as the information laid in two fields of the provided log files.

Patterning chart

The two attributes (fields) that were present in the log files are marked here in blue.

The TAC (the first part of the IMEI code) may be directly used, as it relates to one device model; the master database is maintained by the GSMA, and is delivered to its members (including Telecom Operators).

The User Agent is more complicated, as it includes entangled information; parsing the User Agent will allow namely to identify browser, OS and type of connection that have been used. Still, it does not require additional information, only a good content analysis and a solid set of coding rules.

The combination of these four items creates a unique identifier, which is not specifically related to given users, but creates homogeneous groups, sharing similar technical conditions (hardware, software, network). Each group will then receive contents adapted to their specific conditions, thereby optimizing their browsing experience and consequently increasing engagement.

As this blog is aiming at a large public, I chose to keep a rather simple example. Of course, should the matter be more intricate, the skills I have built up over my years of experience in Data Management will even be more valuable. Feel free to ask more about patterning or other fields of Data Elicitation, I shall be glad to elaborate customized solutions for your business.

Data Elicitation in three steps (2/3): Data Enrichment

The second step of elicitation is enrichment. Once your data have been patterned, you have to design their look and feel. This is what data enrichment is about.

Again key questions in this area:

  • How do I qualify my data? → a categorization scheme is key to facilitate relevant data extraction for future analysis
  • What may be missing or on the contrary is uselessly kept? → choosing one’s data set is necessary for correct data acquisition
  • What type of addition is relevant and what is really useful? → as existing information may not be sufficient for achieving marketing studies
  • How do I acquire additional attributes at an optimum price? → collect, derive or generate additional data at proper cost

No question, your data are rich, especially if you can use them easily thanks to an appropriate patterning. But they certainly can be richer. Much richer. And there are hundreds of ways to enrich data, but only two dimensions to consider, quantity and quality.

You may have tons of data, and still this may not fit your purposes. Or on the contrary have scarce resources, but with a very high (and maybe hidden) value. Market Research companies used to name this data enrichment processing “coding the dictionary”, a phrase showing the richness of this process, both on the quantity (the number of words) and on the quality side (the clarity of the definitions). Getting the relevance out of the data is definitely a precious skill, and one of my own key proficiencies.

I shall definitely develop both aspects of data enrichment in future posts but I wanted to cover them shortly in this introduction.

1. Quantity

One always seems to be missing data. More e-mails for more direct marketing contacts, more socio-demographics for a better segmentation, more inputs from the sales force for a more precise CRM, more, more, more…

As usual, this may be true. Or not! Is Facebook the better source for reaching a specific population? Sure no. For instance, should you want to reach people affected by albinism in North America, you would probably rather get in touch with the NOAH. So, it depends on the purpose. And on your means to leverage a big amount of data.

Of course, I shall not dispute that a large database will give you more opportunities for reaching your targets. But better do it with the maximum level of quality. I shall then cover such topics as coverage, census vs. sample, long tail later on, as dealing with large databases is mostly a question of finding out the right data in the right timing.

2. Quality

A good quality is the heir of a proper patterning. And quality always is the key to an efficient database. The specificity of quality improvement also is that it implies all records, old and new. Unlike for quantity (adding new records is an ongoing task, you seldom look back on past data), quality always requires to give a look ahead AND behind. Adding a new feature, adjusting existing attributes to new constraints, redesigning existing concepts, all this implies a full database review.

I shall cover methods and tips for improving one’s database also in the future. Still the best piece of advice I can give is simple: think twice before starting. I have added below a simple example about the long tail of the internet.

Long Tail

This chart shows a top 1,000 websites, ranked on their visits for a given time period, using their share of visit. The metrics itself is not interesting, rather the data distribution.

The top 50 of the websites (5% of the total records), very well-known, will allow a fair coverage of the activity, e.g. more or less 60% of the visits. So for a small set of data, with a high level of recognition, we could have a good understanding of the activity. Good for global strategy and high-level analysis.

Still, on the other hand the bottom 500 websites account for more or less 1.5% of the visits. Too costly to reach for if you are on a global strategy level, but of the highest interest if you are searching for a niche or a specific type of target audience.

There is no point in balancing between a small database of high quality, and an extra-large one with a high sparsity. Again, the point is to have the database calibrated for your needs. And then enrich it wisely. And you know it by now, I have already told you: this is exactly what Data Elicitation is about!

Data Elicitation : my professional new start in 2014

As you could read it last week in Revival of a digital non-nativeI am now more qualified than ever in Digital Analytics, ready to write the first pages of my professional new start.

It has been very nice to receive a high number of positive feedback and to state the concrete interest my last post has aroused. As announced last week, I shall now elaborate what I am at. This post defines the core business of Data Elicitation. Further ones (in a series of 3) will give much more details about specific contributions closely linked with my own proficiency, and answering concerns expressed by marketers, namely through this study by StrongView (2014 Marketing Survey). Key areas are:

1. Data patterning

The original sin of Big Data is its formlessness. So as to be able to use these data, and get the best out of them, one must organize and structure them first. This is what patterning is about.

Or course, your engineers will claim they have built the best database ever, and that it should answer any question you have. This may be true. Or not. Actually, many databases are built under technical constraints, with very little regard to usage and user experience, let alone to marketing and strategy needs. My own experience testifies that an efficient use of data is first built upon a correct understanding of the client requests, i.e. that the initial step is not drawing the plan, but thinking about how it would best fit its goal. This always has been a key driver of my action, especially when building up various new services in the marketing information business. I am a resolute data patterner.

2. Data enrichment

Your data are rich, especially if you can use them easily thanks to an appropriate patterning. But they certainly can be richer. Much richer. And most certainly not at high costs. This is what enrichment is all about.

You may have tons of data, and still this may not fit your purposes. Or on the contrary have small databases, but with a very high (and maybe hidden) value. And enriching is not only adding external information, it is also deriving, translating, cross-checking existing sources. Market Research companies used to name this data enrichment process “coding the dictionary”, a phrase showing the vastness and complexity of this process.  Getting the relevance out of the data is definitely a precious skill, and one of my own key proficiencies.

3. Data analytics

Now, your data are accessible and usable. Fine. And what next? Getting the best out of your data is not always easy, as the meaning may either be blurred or the solution to the problem lost as a needle in a haystack. This is what analytics are about.

And once your data are fit for use, you need to find the proper tactics and strategy to reach your goal, i.e. get them to talk and find the solution to your issues or validate your assumptions. This requires a fair analytic technique, but also a good flair for identifying where the gems are hidden… In this respect, as a seasoned market research expert with a solid digital background, I shall help you identify where to dig to get the best out of your data.

So in the end, this whole process of patterning, enriching and analyzing data may be summarized under one single word: elicitation. I have chosen Data Elicitation as an umbrella designation for running all these processes and bringing them together as a service.

On a practical level, my door remains open to any CEO who would require my exclusive working force to set up their data marketing corporate strategy (e.g. hire me). Still, the current market conditions, notably in France, imply that flexibility is key, especially in the light of project-driven action. This is why I offer my (invaluable) resource also as a contractor. So? Drown in data? Or searching them desperately? And in need of elicitation? Let us keep in touch and let 2014 be the year for your ultimate data elicitation!