Data Landscape – Navigating The Data Jungle

Taming Your Data Landscape / Jungle

A closer look at the importance (and transformational value) of your organisation’s data landscape

After decades in the background, data is currently king of the business world. Visionary companies like Google and Amazon are renowned for figuring out the transformational power of data, using data-driven business models to achieve extraordinary success.

Now everyone, from government agencies and energy companies to manufacturers and century-old corporations, is rushing to follow their example and exploit the value of their own data.

For many companies, this pursuit has quickly become a case of “easier said than done.” Over 70% of digital transformations fail, and most CDOs last less than two-and-half years.

Why?

We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching data transformation in a way that addresses the root causes of data problems and leads to tangible results and business success.

It doesn’t have to be this way. It’s possible to finally break the bad data cycle and achieve true data transformation — and it all starts with gaining a fundamental understanding of your organisation’s data landscape.

In this post, we’ll explore what a data landscape is, why it’s so important, and how you can design and build a data landscape that supports your business goals.

What is a data landscape?

The definition of data landscape differs depending on the context and who you ask. There are three common ways to look at and define the “data landscape”:

    • Some people use the term to describe the ever-growing $157 billion-plus global data management market, with a heavy emphasis on navigating the complex world of data technology.
    • Others think of the “data landscape” as an internal map or catalog of an organisation’s datasets, systems, and technical quality. This type of data landscape is usually created using innovative data discovery tools like Amundsen, Nemo, and DataHub as part of a company’s data transformation efforts.
    • Finally, “data landscape” is also often used to refer broadly to the links between an organisation’s data and how it gets used by customers, partners, suppliers, and other external stakeholders.

There’s nothing wrong with those definitions per se, but each falls short of being truly helpful in terms of helping an organisation unlock the power of data and use it to achieve better business results. 

The first definition maps the renowned suppliers, grouped by the solution they offer, standing at the ready to help organisations manage their data. It is not a map of your organisation’s data. The second way of looking at the data landscape is about identifying what data you have, understanding where it resides and flows within an organisation’s infrastructure. However, it’s a very IT-focused perspective that resonates most with data specialists and doesn’t connect data to business value.

As for the third definition of data landscape, the intent to connect data to customers is admirable, but it’s still not truly value-oriented and is often too high-level to be useful in practice.

In this article, we’d like to propose a fourth, slightly different way of defining the data landscape within your organisation. It’s a more holistic view than the definitions we discussed earlier and involves a mindset shift to move beyond technical detail and consider data transformation in the context of quantifiable business value.

Using this definition, your data landscape includes, but is not limited to:

  • Data sources such as temperature sensors, cameras, heart rate monitors, GPS trackers, and any other source of the data your business uses to create value for both internal and external stakeholders
  • Data stores including databases and the specific tables where data is located once it’s collected
  • The ETL (extract, transform, load) processes that pump data from one place to another and ensure it is fit for its intended purpose
  • The use cases and customer outcomes your data supports and the quantifiable value your data creates for the business

How does defining data landscape in this way help your organisation? It gives you the scope and perspective you need to map your current data landscape, identify gaps, connect data to business value and define what a data landscape that supports your business goals should look like.

And that’s important. A well-defined data landscape is a map that helps you identify which data is most critical to achieving your business objectives and ensures the right data is available, accessible, and otherwise fit for purpose. In the next section, we’ll discuss more about why your data landscape is so vital to your company’s success.

Why your data landscape matters

Your data landscape is the foundation on which the success of your organisation’s data plans depends. Without the proper focus on understanding and managing your data landscape, you will never become data-driven or be able to use the power of data to transform the future of your business.

That’s because the very heart of a healthy and high-performing data landscape is a clear understanding of your data and its role in achieving successful outcomes for your business and your customers. You must make sure you identify the right data for critical outcomes and make sure it is available and fit for purpose.

If you choose to ignore or rush through gaining a robust understanding of your data landscape, all the fancy analytics tools and artificial intelligence and machine learning algorithms and highly-paid data scientists in the world can’t save you from persistent data problems such as:

  • Investing in technology only to find you can’t use the tools due to the state of your data
  • Suffering from “garbage in, garbage out” syndrome with bad or misapplied data leading to incorrect or irrelevant results
  • Spending money on data projects that miss the mark and yield no business ROI
  • Becoming “data drunk” in an attempt to solve problems by collecting more data without addressing root-cause issues
  • Repeated failed attempts to leverage data to achieve better business outcomes
  • Pressure from your organisation’s leadership to find a magic bullet that solves all your data woes — and to explain why it hasn’t happened already

In contrast, a well-defined and robust data landscape gives you the understanding and supporting framework you need to:

  • Communicate the value and role of your company’s data in achieving successful business and customer outcomes
  • Identify which data is most valuable to your business and prioritise data investment accordingly
  • Build an evidence-based business case for change
  • Understand the root cause of your biggest data challenges
  • Execute data projects that deliver measurable results and ROI
  • Create alignment between business and IT teams on data strategy and goals
  • Secure the buy-in and support of executive leadership

Why it’s so easy to find yourself with a data jungle instead of a navigable landscape

Despite the importance and benefits of having a well-defined data landscape, many data leaders find themselves in charge of more of a “data jungle” in which the data flows have become convoluted. When this happens, no one really understands what data exists, how it’s used, whether it’s fit for purpose, or how it affects customer outcomes.

Sound familiar? Data jungles are an unfortunately common and frustrating reality for CDOs trying to map out a path to data success for their organisations. Let’s take a look at three reasons why it’s so easy to end up with a data disaster instead of the well-defined landscape you need.

1. Organic, uncoordinated data landscape growth

Most data landscapes grow in a piecemeal fashion. Here’s how it plays out:

One group decides to start collecting and storing data just in case it might help solve an important business problem like increasing cybersecurity or predicting the failure of a part before it happens.

Another group in the same organisation identifies a clear use case for a specific type of data and builds the plumbing they need to collect and use it.

Because there’s no structure around the people and processes involved with the organisation’s data, the two groups do not communicate and have no idea what the other is doing. They may even be collecting the same data twice or duplicating existing data stores.

As this situation repeats itself over the years, the data landscape begins to resemble an archaeological dig site with the jumbled remains of multiple civilisations. It becomes almost impossible to map and understand without spending years untangling the mess.

2. Leaving data to the data specialists instead of involving the business

It may seem counterintuitive, but you should never entrust your data landscape solely to data specialists or IT teams.

Why not?

Because, while data specialists are experts in data management and analysis techniques, they do not understand how your organisation’s data should be used, who uses it, or how it adds value to your business. Without a good grasp of things like data dependencies, data flow, and data use cases, well-meaning data specialists can make small tweaks to data pipelines or stores that result in major problems for end-users.

The best way to avoid this problem and ensure your organisation has a well-understood and well-defined data landscape is to treat data as a business problem and include the business in all data discussions, projects, and strategies.

3. Treating technology as the answer to all your data problems

We don’t want you to misunderstand — technology has a lot of valuable uses. Data lineage and discovery tools can help you understand how data flows in your landscape and identify the source of technical problems. Data analytics tools enable you to visualise data and use it to make smart decisions.

But they provide minimal value if you attempt to deploy them without understanding your data landscape. Technology alone can’t tell you which data delivers the most value to your customers or whether you’re getting good returns on your data investments. A fancy tech stack doesn’t help you identify and solve the root causes of your data problems or communicate the potential value of your data in a business context.

Repeatedly investing in the latest and greatest data technology without stopping to examine your data landscape and understand the links between data, value drivers, and business outcomes makes your organisation’s data problems worse instead of better.

Taming the data jungle: how to turn your data landscape from a problem into a valuable asset

A thorough understanding of your data landscape is essential to your organisation’s success — and to your success in your role as a CDO or other data leader. Doing so is hard work, and we’ve seen many organisations that never figure it out and, as a result, never unlock the transformative value of data.

But it can be done. Here are some steps you can take to start defining your data landscape in a way that supports your organisation’s strategic business goals:

Start treating data as a business asset instead of an IT problem

The first and most important step towards turning your organisation’s data landscape from a tangled data jungle into a well-defined set of systems, processes, and business value drivers is to stop thinking of your data as an IT problem or a periphery business input.

You must recognise and treat your data as a valuable business asset to be managed and invested in like other more traditional assets.

What’s the difference?

IT problems get handed over to specialists to “deal with”, while business assets get the focused time, energy, and resources needed to maximise their value.

When you treat your data as an asset, data problems become business problems. This allows you to move beyond technology solutions and collaborate with the business to better understand how well your data landscape is (or isn’t) supporting your organisation’s strategic objectives and goals and identify areas for improvement.

Focus on value

As we’ve discussed, a focus on quantifiable business value is the missing element in many common data landscape definitions. If you want to get your organisation’s data landscape “right” you must start with what’s most important in terms of creating business value.

Instead of spending all your time building technical maps of your data and detailing 1000 different datasets, consider the different ways your data is used, and how those use cases create value for your organisation.

But don’t stop there — qualitative data valuation isn’t enough to allow you to fully understand or communicate the value your data landscape drives. Quantitative data valuation is a necessary part of mapping your data landscape.

Using this value-centric approach, you’ll be able to identify the most valuable data and relationships in your data landscape and place them in a proper business context, which is a huge step forward in your quest to design and build a data landscape that powers business success.

Embrace the concept of data condition

Once you’ve firmly established a well-defined data landscape as a business necessity and adopted a value-focused approach, you should turn your attention to data condition. Data condition is a measure of how fit your data is for its intended business purpose. It’s not as well-known as some other approaches to data, but it’s an essential tool for navigating your data landscape.

Assessing the condition of your data includes evaluating the data itself as well as the systems supporting it and the broader business context. In other words, measuring data condition requires a deep dive into your data landscape.

In combination with treating data as a business problem and focusing on value, considering your data landscape through the fit for purpose lens of data condition significantly boosts your data landscape definition efforts by allowing you to:

  • Identify exactly where your data is supporting successful business outcomes and where it falls short
  • Frame data problems and value in a language the business can understand
  • Provide a common framework for discussion and cooperation between business and IT teams
  • Expose how the current state of your data landscape is affecting your business
  • Gain the insight you need to establish and communicate a quantifiable case for change
  • Prioritise your data investments based on business value and expected ROI
  • Repeat the process as needed to continually evaluate your data landscape and measure the success of your improvement efforts

Ready to get a better understanding of your organisation’s data landscape? Let’s Talk

We hope you’ve found this article helpful in gaining a better understanding of how to define and navigate your organisation’s data landscape. If you have questions or want to know more about how we can help you get value from your data, contact us. We would love to hear more about your business.

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