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Our economy is digital. Data is what fuels it.
5 reasons why data needs to be approached strategically:
- Data is the most valuable economic asset
- Data grows exponentially
- Technology is commoditizing at an increasing pace
- People and systems come and go, data stays
- Increasingly stricter privacy and data regulations are being adopted all around the world
Not having a data strategy in 2021 is like driving on a night highway with your headlights off. You are bound to crash at some point.
As every business is now a data business, a strategic approach to data enterprise-wide is a matter of not just efficiency, but organization’s survival.
Why organizations need to adopt an enterprise data strategy.
1. Data is a valuable economic asset.
Data is the currency of the digital economy. It is what fuels it.
As Jonathan Knowles explains in this article, 70% of today’s economy consists of intangible assets, i.e. data & information. It is easy to predict that the share of the digital economy is only going to increase. But you shouldn’t think about the value of data in terms of “price per gigabyte”. You should think in terms of value per use case, or value per action.
Data has unique economic properties. It is non-depleting – you can use and reuse it an infinite amount of times. It is also non-rivalrous – you can consume it in multiple scenarios simultaneously.
A good enterprise data strategy needs to address these economic properties at scale.
Data is the new commodity. It’s the new raw material to be endlessly mined from the digital realm and exploited to extract economic benefits. Despite all the popular comparisons, the value of data is not at the level of gold or oil. It’s way above it.
The digital economy is just getting started. There is so much economic value that can be unlocked using data.
You can read more about the value of data in Doug Laney’s “Infonomics“, or Bill Schmarzo’s “The Economics of Data, Analytics and Digital Transformation“.
2. Data grows exponentially
The amount of data in the world is estimated to be doubling every 2.5 years.
If you haven’t noticed this exponential growth of data in your industry, you are not paying attention. While in some areas (for example, that involve IoT, visual information capture or AI-based data generation) the data production volumes are drastically larger, every industry has seen this exponential curve.
Both humans and robots are generating increasingly more data all around the world.
Besides just the sheer volume of data, the complexity of it increases as well in terms of the diversity of data sources and formats.
Another thing to note is that using data often produces more data.
This exponential growth brings about a lot of complexity to the management and analysis of data at scale.
Organizational data strategies need to account for these dynamics to make sure the business remains competitive and relevant to market needs.
3. Technology is commoditizing at an increasing pace
Technology-wise, it has never been cheaper to collect, store and process data.
The pace of innovation is immense. Any technology that gets a decent level of adoption is quickly becoming commoditized.
Cloud has changed the way organizations manage and host their data. It offers unprecedented opportunities for increasing the agility, scalability and integration of tech stacks.
On the other hand, technology shelf-life is decreasing. Tech vendors that fail to adopt a rapid innovation rate are on their way out.
Increasingly, a lot of tech requires less coding. Technology is also becoming smarter with the use of AI. The “ambient intelligence” (a term I borrowed from Satya Nadella’s 2021 Ignite keynote) is perpetrating more and more software platforms every day. For instance, analytics and data science platforms are becoming more seamless, user-friendly and intuitive, e.g. through the use of natural language processing (NLP).
Besides things becoming cheaper and easier to procure and adopt, there is also a consolidation trend. Most platforms are integrating new features and functionality to cover more of the information lifecycle. For example, data lakes and data warehouses are converging, becoming “lakehouses” and offering user-friendly ways of combining the ETL and ELT worlds.
Not all technology is good from data economics POV though. There are still many platforms and tools that will lock your data in or limit the use and reuse in a variety of ways. You should avoid them.
4. People and systems come and go, data stays
As businesses evolve, their org structures and technology landscapes change. What typically survives staff and technology turnover is data. In fact, the core data of most organizations is likely to outlive the organizations themselves.
My experience of over 20 years in tech and data has led me to believe that technology lifecycles should be managed separately from information lifecycles, which are inherently different. Technology is more volatile than information. This means that technology investment is about speed, while information investment is about resilience.
There are multiple ways to identify and manage information lifecycles, from traditional data management and governance approaches, to more modern techniques augmented with the power of knowledge graphs and machine learning. Most of them require organizations becoming savvy at managing metadata.
It is also necessary to have a dedicated data department. One of their primary tasks is to manage and preserve organizational data through org and technology changes. Data departments are also helping to maximize the reuse of data through governed democratization.
An organization’s data strategy should enable resilience and trust for its information, while enabling agility for its technology.
Without this distinction, you are likely to get stuck with easily outdated technology stacks that are not prepared to address the new way of working. You are also liable to spend scarce resources on maintaining technologies that are doomed to become obsolete quickly.
See Dave McComb’s “Software Wasteland” and “Data-Centric Revolution” for a deeper dive into this topic.
5. Increasingly stricter privacy and data regulations are being adopted all around the world
The economic value of data has become so high that it can overshadow other aspects, such as ethics and privacy. Security is a key area of concern too, as the cybercrime is at an all-time high. As a result, governments are adopting stringent regulations to protect data and personal information.
If you are not paying attention to data privacy and security, your company is exposing itself to risks.
The EU GDPR (General Data Protection Regulation) is the biggest example. It entered into force in May of 2018. The fines for non-compliance are up to €20 Million or 4% of the global annual turnover of an organization that has committed a breach. In Q3 of 2021 GDPR fines exceeded $1.1 billion. You can find the full list of fines with statistics here.
A comprehensive list of privacy regulation is available at Data Collaboration Alliance’s Data Privacy Grid.
Your data strategy should account for the larger trends of data privacy and security and enable your firm to be compliant.
A business without a data strategy will find itself at the mercy of others, unable to harness the power of its own data.
Unfortunately, most businesses have evolved to be system-centric. They think in coordinates of systems, platforms and tools. Data is often an afterthought and can easily turn from an asset to a burden.
Rigid organizational structures also make it difficult to be agile with information.
The key to success for any company is now, more than ever, understanding and acting on this information.
A data strategy is more than just addressing regulatory pressures or even increasing efficiency. It is about identifying where you stand in the data economy and how you plan to leverage data for differentiating your offering and your business model.
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