No “one size fits all”—make the most of your data ecosystem

Reko Lehti
Executive Partner
August 12, 2019

The days of making important decisions based on gut feeling are over. With an increased amount of data being collected at ever-accelerating rates, there has never been a moment when this much data has existed. It has been predicted that the data stored in data centres will quintuple by 2021 and reach 1.3 zettabytes globally.[i] This is the reason why many companies want to be more data-driven in their strategy and business operations. Making the most out of the available data is key to keep their position in the market.

This also means that the global data ecosystem – a network of actors that produce or provide data – has never been as big as it is now. What opportunities does this open to businesses? Let’s take a look.

Many companies are increasingly interested in the possibility of incorporating external sources of data into their analyses to fill in the information gaps their internally generated data cannot provide. Almost half of the companies recently surveyed by Gartner reported using external data in their analytics activities[ii] and 92% of 800 companies surveyed by Forrester agreed that the rise in digital technologies and interactions has increased the need for bringing outside data into the company[iii].

Without the use of external data, companies’ internal decision making is often done without fully considering the customer’s environment. Since one of the megatrends influencing every single industry is the fierce urgency to become more customer-centric, not making the most of external data would be a very short-sighted move. Also, a significant part of innovation and R&D of technology such as machine learning relies on the availability of huge amounts of training data and open datasets.

Despite the trend of democratizing data science and providing access to data in data-lakes, cloud-based platforms or in-memory computing close to the users there are still some challenges.

According to a survey commissioned by Pitney Bowes[iv], global leaders still struggle to access and effectively use third-party data. The main challenges of using external data are related to

–    Improving the quality and accuracy of data

–    Maintaining the quality of data as it changes

–    Improving the ability to detect and track changes in data

Other barriers were related to data acquisition, especially the high cost of licensing reliable data sets from third parties, the timeliness and reliability of open datasets and finding the right data products in the open market with seemingly endless choices. With more and more distributed systems on the market helping in making sense of big data, how can you decide which is the best choice for you?

It can be difficult to compare, or even distinguish, the technical compatibility of several options when so many of them are available, yet companies need to resolve the inconsistencies between external and internal data before being able to analyse and use it to create value.

Intermediary players facilitating data exchange

What mechanisms are available for companies that decide to make some of their data available to others or companies that want to get access to third-party data?

According to the survey commissioned by Pitney Bowes, the preferred ways for accessing data are through APIs and self-service interfaces, but 99% of the organisations say that in future they will be open to purchasing data through online marketplaces[v].

Data sharing platforms have an important role as intermediary players facilitating data exchange. They provide the technical infrastructure for the exchange of data between multiple parties, lower transaction costs by combining different data sources and help data users and suppliers to find each other.

It is important to note that data platforms are set up and owned by businesses that have different incentives and strategic development ideas.

There is a distinction between platforms set up by the companies sharing the data as opposed to platforms provided by third parties. Company-owned platforms can act as distribution channels permitting the monetisation of company’s data to create additional revenue, whereas the benefits of independent third-party platforms are large for companies that either cannot build their own platform or if sharing the data on a neutral platform brings additional benefits. Therefore, independent third-party platforms can attract a wide customer base and offer the possibility to create new value by monetizing the data and selling it to customers.

Another parameter to consider is how open the platform is for new participants. Some platforms can be closed in a sense they are limited to certain cooperating partners or exert strong and selective control over who can participate in data sharing. A challenge is for the users of the platform to understand the value of the data. If the users do not realise that, the platform has no use.

The types of most sought-after datasets also vary by region. According to the results of the survey commissioned by Pitney Bowes[vi], survey participants in the U.S. say they purchase third party data to obtain customer behaviour data (60%) and geographic or geo-demographic data 60%), whereas in Canada, the participants expressed their interest in digital data (51%), social media data (45%) and customer behaviour data (45%). In the UK, the most sought-after type of data was social media data (59%) with digital data (58%), and in Australia, participants were looking for transactional data (54%) and customer behaviour data (43%).


Not a single company that wants to be leading in its field can afford not considering the data ecosystem surrounding it. As more and more companies face increasing pressure to become more data-driven in their strategy and business operations, here is what to consider when building a well-functioning data ecosystem:

  1. Strategic clarity
  • Decide what business problems you want to tackle using data
  • Demand Key Performance Indicators and data-driven decision-making
  • Know your key strengths and when to collaborate and— most importantly—how to collaborate
  1. Data ecosystem and business architecture
  • Create a map of your company’s data infrastructure and ecosystem to better understand where, and how, the use of data creates value
  • Invest in the technical interoperability standards that enable producing, providing, and analysing data of good quality across the company
  • Make sure the infrastructure of your data ecosystem is coherent and flexible
  1. Organisational culture and value creation
  • Create an interactive, collaborative and data-driven culture across the whole company
  • Use data-driven analytics to develop your business model by monetising your data

Ultimately, there is no “one size fits all” approach to building and orchestrating a successful ecosystem that would create true value for all ecosystem members.

What are the steps you are going to take to make the most out of your data ecosystem?

This article is from our series of “Data-Enabled Business”. Check out our pieces about unlocking the value of data and data operating model.

[i] Cisco, “Cisco Global Cloud Index: Forecast and methodology, 2016–2021 white paper,” November 19, 2018.
[ii] Gartner, “Gartner survey shows organizations are slow to advance in data and analytics,” press release, February 5, 2018
[iii] Forrester Consulting Thought Leadership Paper “Digital Is Driving The Next Generation of Data Marketplaces”, Thought Leadership Paper, commissioned by Pitney Bowes, December 2017 [accessed 15.7.2019] [iv] Pitney Bowes, “Survey reveals global leaders struggle to access and effectively use data,” press release, December 12, 2017.
[v] Forrester Consulting Thought Leadership Paper “Digital Is Driving The Next Generation of Data Marketplaces”, Thought Leadership Paper, commissioned by Pitney Bowes, December 2017 [accessed 15.7.2019] [vi] Pitney Bowes, “Survey reveals global leaders struggle to access and effectively use data,” press release, December 12, 2017.


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