Data investments

Strategy to invest in data to be able to monetize its value

Understanding the value of internal & external data is part of any data strategy. Organisations looking for an investment with high potential yield, must think ‘data.’

However, understanding it on a granular level, sufficient enough to drive new data products or realize an automated data pipeline is still in its infancy.

What is required to understand the value of data?

• Investigate your data;
• The need for data;
• Invest in data;
• Understand what is needed to accelerate.

1. Investigate your data; what are you using and what is missing?
   
   Understanding the data that your organization captures, stores & consumes is challenging. Data is at differences locations, meaning something different (very much depending on objectives!) to different stakeholders. Most likely multiple instances of data from the same system or even external suppliers exist across a single organization. Spotting these overlaps is a significant opportunity in cost savings, faster R&D and time to market for new products and efficiency gains by streamlining data pipelines. Determine which data is used overarching units, systems and products. Classify this as ‘corporate’ data, data that is the most important to your business. Value and guard this data well!
   Also investigate the data gap, what data you are missing and why. Is data not captured due to insufficient data quality regime? Are external data suppliers unknown to the organisation? Is the business case for data purchasing not defined? Resolve this by having a central plan of approach which fits your business strategy.
2. The need for (external) data
   Tying together an organisation’s data ecosystem is crucial to becoming data-driven. It is also the data that is most predictable for your team and with coordinated effort, it can be completely under the control of the organisation itself. Furthermore, by being able to analyze all the data under your own roof, you get the benefit of better process standards, deeper knowledge of your client base.
   Understanding which data you need enforces getting mechanisms in place to understand data consumption and data movement. Powerful stuff.
   There is an entire universe of data available in the market (see also: #Data hunting), based on trusted data principles such as standardisation, (privacy) legislation, data quality and availability. Which makes it easier to transfer it into the own systems and applications of the organisation, where it will be transformed to meet required schema’s.
3. Invest in data
   Pouring money into the stock market won’t yield you returns if you’re buying without a plan — you’ve got to be smart and methodical. By the same token, you can spend a lot of money on data without getting much out of it.
   Change the mindset: step away for the idea that data is an expense. Data is one of the new foundations for the business strategy for monetization (see also: #Data monetization). And data needs more than one good product or service to be a smart investment.
   Short side-step: A few years back, the initial approach for data investments whas acquiring as much data as possible and storing it into a data lake. This is not sufficient anymore (I was not sufficient in the beginning as well — this can be an expensive lessons learned).
   The concept of ‘trusted data’ will enable organisations to cut expenses, because it will enable having sufficient data centrally available. Acquiring the right data isn’t a one-off. Organisations are looking for ways to re-use up-to-date, correct and timely data for multiple purposes. Trusted data will also reduce e.g. data prepping for AI, manual data pipelines and labor intensive data quality monitoring.
4. Understand what is needed to accelerate your investment.
   Infrastructure: having sufficient data accessible through a common platform enables speaking the same language, timely availability of data and consistent data quality.
   Data quality & consistency: monitor acquired data to resolve bad data and changes to data format and definitions through a central data catalog, a data lineage and standard process for data consumption.