Nearly every business now has a company-wide programme to help it capitalize on recent advances in digital technology.
Commonly known as ‘digital transformation‘, this process could (and perhaps should) be thought of as ‘data-driven transformation’ considering how important data is to the whole process.
Reason being, unless data is captured, shared and utilized, any increased adoption of digital technology will be in vain. A competitor with the better data will most likely win in the end.
With this in mind, how can companies transform both digitally and with data? What are the issues that need to be considered?
To find out, Econsultancy recently invited dozens of marketers to discuss this and other topics over roundtable discussions. At a table hosted by data experts David Brigham, Analytics Director, Mirum, and Zak Agus, Sales Director, SEA, Tealium, brand marketers revealed their thoughts on what was driving data-driven transformation at their organisations. The main points from the discussions are summarized below.
So what do organisations need to consider when driving data-driven transformation?
1) Breaking down data silos
The first thing participants asserted was that there is no ‘one size fits all’ approach to getting data flowing through an organisation. Every organisation is different and so each data-driven transformation project needs to take the organisational structure into consideration first.
Yet despite these differences, nearly everyone said that departmental data silos are perhaps the biggest barrier for companies aiming to harness the power of data.
In order to get over this hurdle, participants argued, the transformation team must examine all of the company’s data assets and determine which department has ownership and who already uses the data. Then, they should ensure that transformation has buy-in at the highest level so that management cannot unreasonably stand in the way of future data requests.
2) Breaking down insight silos
Data silos, however, are not the only problem faced by data-transformation teams. Another issue which attendees brought up is that companies also have ‘insight silos’.
An insight silo occurs when one department has data analysis expertise which is lacking in other areas of the organisation, and is unwilling or unable to share.
According to attendees, this situation occurs quite often at airlines. Airlines have large teams of pricing analysts and separate teams of marketing analysts who work independently of each other, with little sharing of insights.
So, for organisations to benefit from their talent, the data-transformation team should also identify where the analyst talent sits and find ways to get the teams to collaborate.
3) Finding industry ‘data pools’
For data that does not exist within the organisation, the transformation team will have to look elsewhere.
Existing solutions, such as data management platforms (DMPs), help but often they are expensive and may actually offer too much data.
A new alternative to DMPs, according to participants, is for companies in the same vertical to share data between each other so that everyone benefits from having access to relevant and relatively inexpensive data.
Named ‘data pools’ by our subject matter experts, these new ways of obtaining data inexpensively should also be researched by the data transformation team.
4) Resourcing the transformation
In addition to identifying the talent already present within the organisation, participants indicated that data-driven transformation often requires people with new skills, such as data scientists.
As finding the right people for these roles is often time-consuming and difficult, one suggestion was that an organisation going through transformation should first hire a ‘data guru’ who will be responsible for upskilling existing staff. In this way, expertise can be built-up in-house at the same time new talent is being recruited.
5) Proving data-driven ROI
Proving return on investment (ROI) is now expected in most marketing departments, but it is a relatively new topic for analysts.
For a small project costing a few thousand dollars and lasting 3 months, ROI may not be a big issue but for a long-term data transformation costing a few million dollars, ROI should certainly be a consideration.
To manage this requirement for ROI, data-driven transformation teams should be prepared to defend their investments in technology and resources. The team will need to demonstrate how their approach will either increase revenue or decrease costs, something which most analysts have not yet given much thought, said one participant.
6) Communicating the limits of data-driven transformation
Finally, attendees said that the transformation team should set departmental and management expectations about the potential and the limits of data-driven transformation.
For example, a marketing department who wants to invest in personalisation data must first understand the risks of becoming too intrusive through excessive use of personal data.
Additionally, there will come a point when additional data and analytics will only achieve incremental results, and business heads must be made aware when that point is reached. As one participant put it, “you can’t continually invest in data and expect to get the same results every time.”
So, while teams are talking up the potential of a data-driven transformation they must also be careful not to overpromise and subsequently underdeliver.
A word of thanks
Econsultancy would like to thank our table host David Brigham, Analytics Director, Mirum and subject matter expert Zak Agus, Sales Director, SEA, Tealium for guiding the discussion and providing real-world examples of how brands are achieving marketing automation excellence.
We’d also like to thank the table sponsor, Tealium, and all of the marketers who attended Digital Cream Singapore 2017 to share their valuable insights. We hope to see you all at future Econsultancy events!