No data governance, no data intelligence!
By: Hassan Lâasri
Tags: Big Data, Data Analytics, Data Strategy, Data Science, Digital Transformation

Photo by Amanda Jones on Unsplash

Any big traditional company dreams to be like Google, Facebook, or Amazon but there is one thing that separate the former that existed before the birth of the Internet, from the latter which started with the Internet: they were not born with data mining and machine learning in their DNA.

Data, a resource turned into an asset

For the incumbents, data was an information technology (IT) resource to conduct business and as such, it was delegated to CIO and his or her organization to manage it. The latter was missioned to build the global data architecture, choose the database vendor, and design all the necessary applications to process data from the databases to the screens of business and functional leads.

We had the enterprise resource planning (ERP) era to manage data for finance, supply-chain, logistics, and human resources functional organizations. After the ERP era, we had the client management relationship (CRM) era to manage data for marketing, sales, services, and support. Then came the Internet that triggered a new era with company’s websites, social media pages, and downloadable mobile applications.

Unlike the two first eras, the Internet era gave rise to a new type of data and processes that is totally different from what incumbents were used to with ERP and CRM data. Google, Facebook, Amazon, Uber, and Airbnb were born with advanced data mining in their DNA. The scope of their analytics was not only about what happened but also about what is happening and what might happen. This is all what big data and advanced analytics is about.

At the same time, the analytics at the incumbents were limited to what happened. This is what is meant by business intelligence (BI). But things have changed since. Incumbents changed their minds about data from a necessary resource into a valuable asset and started to hire data analytics professionals — with heterogenous titles: statisticians, data miners, data analysts, data scientists… But the incumbents continue lagging behind Google, Facebook, Amazon, and alike who are already in an era of deep learning, a kind of data mining on steroids. For evidence, I invite the reader to cast an eye over the continuous announcements on the Web.

The question a careful reader would have is why the incumbents are still lagging despite their large databases that span decades (case of retailers, banks, and insurances) and their recent investments in big data and data science?

To me, the answer is not about the incumbents that are in darkness, their executives who are not aware about the value of internal data their companies have accumulated since decades, nor their data professionals who are less knowledgeable than the ones at Google, Facebook, and Amazon. It is about the age, the structure, the format, the quality, and the diversity of data they possess. Data in incumbents is much more complex than one might think. So, mining it to extract insights and predictions as the online platforms do, is not automatic. To reach the level of sophistication of the online platforms, the incumbents have no other choice but to add data governance as complement to data management and data science as presented in the following sections.

Data governance, a must-have for data science

Like any new acronym, there is no consensus of its definition. To get an intuitive idea, think of all data preparation work that data scientists do for each project and imagine all of this work done once and regularly updated for all the projects. This is data governance in action.

In a layered data architecture, data governance sits on top of data management (aka relational databases, big data, ETL, cloud storage) and below data science (aka statistical modeling, predictive analytics, machine learning) to cover at least the following functions:

Ingestion, integration, and harmonization of data coming from all the databases that compose the digital nervous system of the company. Depending on the industry and business of the company, the digital nervous system can comprise the digital processes that closely link every aspect of the company’s thoughts and actions — finance, production, customers, and so on — in a single system (rare situation) or split into multiple interconnected systems (common situation)

Measurement and tracking of the quality of data from its capture into client-facing applications (customer relationship management) to its consumption by back-office applications (analytics, reporting)

Application of data privacy regulations (e.g., GDPR, California Consumer Privacy Act, New York Privacy Act) and any other regulations specific to the industry of the company like in health-care, banking, and insurance

Referencing of all external data (purchased or open source) aimed at crossing it with the customer data to infer insights for the marketing departments of the company

Cybersecurity, the biggest threat for companies that now almost all of their client-facing and back-office activities are conducted digitally. To me, cybersecurity should be at the same level of priority if not above digital transformation and business datafication

As the definition implies, data governance is less technical than data management and data science. The bulk of the work at this level is organizational: defining processes, not all of them are automated, and committees to decide on what data to consider, in which order, how to manage it, what to do in case of missing data, how to infer it, when and how to update data in order to keep it always accurate, up-to-date, and of high quality, etc. As these examples of tasks show, it is not because it sounds less glamorous than data science, that it should be overlooked.

As regularly reported, the majority of data projects did get stuck not because they didn’t have the right data scientists or the right models and algorithms. In the reported cases, it was due to a problem of data availability, quality, or diversity. It is not unusual to expect data preparation to be the most time-consuming part of a data mining project. And for this reason, it would be more effective and efficient to have worked out a data governance strategy that serve all the projects (current and planned) for advertising, marketing, sales, and customer relationship management or for finance, supply-chain, operations, and human resources.

Now that I have defined data governance, let us see how everything stacks up to build a data strategy that enable incumbents to reach the level of maturity as Google, Facebook, Amazon, and alike?

Data layered architecture

A layered architecture allows to present data into a set of layers where each layer serves the layer above and guides the layer below. It is more of a representational view than a technology stack.

Data strategy level: At the top level, should be the corporate data strategy that define which data has value to the company. Not all the businesses are equal. For some, the most important data to consider is related to advertising, marketing, sales, and customer relationship management. To others, it is related to finance, supply-chain, logistics, operations, and human resources. Yet to others, data is a product to monetize.

Functions level: Below the data strategy level, should be the functions. For example, if data is related to advertising, marketing, sales, and customer relationship management functions, then the functions could be:

Compute advertising attribution, optimize media planning, and distribute advertising budget

Improve direct marketing campaigns

Use current customers to learn about prospects

Reduce exposure to credit risk

Cross-sell, up-sell, and make recommendations

Reduce customer churn

Forecast the sales

Data science level: below functions level, should the data science level. It is at this level that companies customize or develop models to algorithms that serve the functional objectives. In the same example, data scientists will help to answer the following questions: who will remain a loyal customer and who will not? Which messages are most effective with which segments and on which days? What if the media budget is split half for TV and the other half for outdoor?

To answer such questions and others, there is a need to rely on high-quality data from the databases of the company and it is here where data governance plays its role. Without data governance, even the most sophisticated models and algorithms will not be effective. As it is now admitted, the power of data science resides more in high-quality data than in sophisticated models.

Data governance level: below data science level, data governance enables data scientists to be certain to use data with the highest quality and therefore do not waste their time on useless, outdated, or unworkable data. If the work has been done, then the data preparation for data science will be much shorter than without data governance — Without data governance working, data preparation is estimated to eat up to 80% of data projects.

Data management level: below data governance level lie all the traditional and new data technologies, ranging from relational data to big data to data on the cloud. It is the territory of IT who should align the architecture, the extract-transform-load processes, and the pipelines between the data bases according to what data governance has defined as standards.

Wrap-up

All big traditional companies dream of being like Google, Amazon or Facebook, but they were not born with data mining and machine learning in their DNA. They need to add data governance to their strategy for their data sources to reach the quality required for data mining and machine learning to be effective.

Data governance should lie above data management, to guide it with the required quality, security, and conformance needs, and below data science, to reduce the time and cost of data preparation. As such, all digital transformation and business datafication programs should include data governance layer. Such large programs constitute the best opportunity to tackle this layer, without which there will be no data intelligence.

About the author

Strategy consultant and transition executive in technology, innovation, and transformation with extensive experience in data and tech for advertising, marketing, sales, and customer relationship management.

This paper benefits from strategy and implementation work done for top-tier consumer research firms, digital and data agencies, CPG/FMCG retailers, luxury and beauty brands, and a large mutual insurance.

Comments are welcome. Please, either post them here, send them in private message, or email them to hlaasri at hbmjpartners dot com.

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