Build better systems to organize your business data

Most companies still struggle to manage their data and leverage it. They spend a lot of time and energy, but don’t get much for their efforts. Quality is low, people don’t trust data, technical debt is out of control, and they miss opportunities to become data-driven, leverage advanced analytics and AI, and compete with data. Indeed, most organizations are simply not suited to the rigors of working with data.

This, of course, is a problem. At this point, virtually everyone’s job is to use, interpret, and create data. Yet this seems to get lost at all levels of organizations – the structure, the culture, the people. It is often unclear who owns the accountability data (the CDO? IT? Everyone? Nobody?), and as the data tends to be hidden, in customer orders, logistics and reporting management, the power of the status quo prevails. Without clear expectations, chaos reigns. People don’t know what to do, basic tasks are not done and much of the work undertaken is done poorly. The sad reality: more often than not, data is essentially unmanaged.

Businesses need to design better systems and approaches for working with data, and that starts with clarifying management responsibilities for everyone who touches data in any way, across the enterprise. Here are five guidelines for deciding who should do what when it comes to data.

Get everyone involved.

Most real-world data action involves “everyday people,” who don’t have “data” in their titles. They create the stuff; interpret things; use material to satisfy customers and regulators, track inventory and money, make plans and decisions, etc. These people are effectively on the front line of any larger data project, program or strategy, and are critical to its operation. Yet they are almost always left out at the planning stage. Given the hype around big data, artificial intelligence, and digital transformation, you might be surprised that including ordinary people is the single most important step companies can take to accelerate their data programs.

There is huge potential here. To unleash it, companies need to clarify the roles and responsibilities of ordinary people, as data quality customers and data creators, as small data scientists, as contributors to larger data projects. important, as better decision makers and as guardians of the company. The data. The first step leaders need to take is to put ordinary people and those responsibilities first. They must also monitor, train and support people to help them become effective in their new responsibilities.

Build the infrastructure to work above, around and through silos.

While businesses get the most out of data when it’s used across departments, silos get in the way of data sharing. Despite the fact that almost everyone depends on data created by other departments to do their job (for example, sales use lead data generated by marketing, then passes sales data production operations, etc.), the services are often unconcerned about the quality of the data they transmit. Companies are gigantic garlands of data streams, and when bad data is transmitted, it ruins everything.

For better or worse, silos are probably not going away anytime soon. Instead, companies need to build an infrastructure that can transmit and coordinate the flow of large amounts of data in an organized way – what I call “big organizational pipes” – to deal with it. First, companies need to define and manage data supply chains. Just as companies track the producers and raw materials they rely on to manufacture and deliver physical products, they must define and track how data is created, how it moves from place to place, and how they are analyzed and used along the way.

Second, they must build a data science bridge, which supports communication between business teams and centers of excellence in data science. These two teams often find themselves at cross-purposes, with the former trying to build predictable processes and the latter trying to disrupt that stability to find improvements, update decision-making, and develop new products. The bridge aims to ease this tension, helping both teams understand each other’s concerns and needs.

Third, they must create shared language between departments and within the company. As companies grow and departments specialize, the language used diverges. (For example, the term “customer” comes to mean “prospect” for marketing, “person with signing authority” for sales, and “entity ultimately responsible for paying the invoice” for risk management.) This makes it difficult for people to work across silos and for data scientists to make sense of enterprise data. Businesses can make huge strides by identifying around 150 key concepts that unite them and agreeing on common definitions.

Let IT take care of the technology, not the data.

Too many companies mistakenly assign primary responsibility for data to their IT departments. But most data isn’t created or used by IT – technology and data are different assets, the same way streaming services and movies are different. Companies should let IT do the technologybuild infrastructure capacity, automate well-defined processes, and ultimately reduce technical debt.

Load professional coaching and coordination data teams.

Businesses need small, professional data teams with deep expertise in a range of topics, including data quality, data science, metadata management, privacy, and security to handle these responsibilities day in and day out. As explained in a previous article, perhaps half of the efforts of these data teams should be aimed at training and supporting ordinary people so that they can take on the roles and fulfill the responsibilities described above. Professional assistance is also needed to help those responsible for managing data supply chains and establishing a common language. A network of on-board data managers is key to increasing the reach of professional data teams in conducting this work.

Professional data teams should also reserve some of their time for specialized work – interpreting privacy regulations, developing data models, and leading particularly difficult or important data science initiatives (although these efforts should also involve ordinary people).

Keep senior leaders off the sidelines.

Over the past generation, great data science and data quality methods have proven themselves in countless circumstances, solving tough problems, unlocking new customer insights and reducing costs. Yet it has proven difficult to introduce these new ideas into companies and to extend the successes of one part of a company to others; the failure rate of data science projects remains worrying. Data programs urgently need senior executives to help solve these problems. Yet, by and large, senior leaders have sat on the sidelines.

It seems to me that senior leaders want to do the right things, but they just don’t know what the right things are. In their defense, they face a confusing barrage of proposals, all of which promise disastrous consequences if ignored, but each of which offers different recommendations. Separating the signal from the noise is a big challenge.

With that in mind, I advise leaders to initially focus on two things.

First, make connections: companies are loaded with business problems/opportunities and with big ideas in the data space. But too often they fail to find each other – business issues remain unresolved and those with big ideas get frustrated. Senior leaders are uniquely positioned to connect the two.

Second, the building, over time, of the human capacities required here. If you haven’t already, hire a great data manager, one with the courage of a lion to stand on the front lines of change every day, and with the patience of a saint, to think long term and not get distracted by small snipers.

. . .

These guidelines represent a sea change in the way data is managed today. Some will say they are too hard or not worth it. But data management today just doesn’t do the job. From my perspective, it’s insane to think that just having a center of excellence in data science would lead to ideas that would change the industry, that a few crude PhDs could change the mind of a generation accustomed to managing by the seats of their pants and pantsuits, or that the application of the latest super-fast technology would compensate for the inability to manage data properly for an entire generation.

Electrification provides a useful example. Because electricity did not appear on the scene and magically improved everything. Managers and technicians had to figure out how to manufacture and deliver it safely, how to change the workshop to accommodate it, how to deal with its challenging properties, and how to get everyone to do their part! It took an entire generation. We shouldn’t expect less for the data.

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