With the vast amounts of data companies gather and store, it can be challenging to figure out what’s actually of value. How can we use data more effectively? How can we assess and increase the economic value of data, and get additional revenue from it?
The cost of data redundancy will be $3.3 trillion in 2020. And 21% of businesses will experience reputational damage as a result of redundant data.
Many organisations talk about doing more with their data – ie your data – by using big data but they first need to decide what they want to achieve. This may be improving processes or reducing costs, but currently there’s a lot of disparity between big data and what’s actionable in terms of revenue-based outcomes.
We need to differentiate between data used on a day-to-day basis, for a work process or transaction, versus data that is a core asset to an organisation.
For example, if you onboard a customer with a sales value to the organisation of $100, that data has an economic value of $100. If you take that $100 and then put it through big data and determine that you can upsell or cross-sell to that customer, eg another $20 because you have a complementary product, the economic value of that data is now $120.
What you’re doing is increasing the value of your data by supplementing it with analytics, better data quality, and deeper customer understanding. And as you enhance that data, it should become a more valuable asset. Its revenue potential should continue to grow, or at least not drop over time.
The challenge, then, is to identify data within the organisation that represents real economic value in the real world. Next, determine how to enhance its economic value and earn more revenue from it.
The questions to ask are:
- Does the data need to be cleansed?
- Does it need to have analytics run over it?
- Do I need to understand its context and provenance, so I can interpret it better or react to it better?
- Do I need to keep it dynamic and updated?
Organisations that don’t want to go it alone can partner with an external party to generate more revenue or an alternative revenue stream.
Digital transformation is not merely about being digital, it’s about finding different revenue streams. Turning data into an asset is a way to do this.
For example, Netflix uses data to recommend other shows, and Amazon uses it to upsell and cross-sell.
If you have multiple versions of the same customer data, and you don’t clean up that data, you end up spamming the customer and getting attrition. Whereas if you clean it up you are treating that data as an asset, and can have a more meaningful conversation with the customer. For instance, one customer might have a credit card, mortgage and savings and business accounts and they end up with four different contact points with a bank, and four different managers cannibalising one another’s portfolios.
In summary, there are four steps that companies can take to address the issue:
- Look at core processes within your organisation and identify information and data attributes that move through core processes, affecting business decisions or driving revenue.
- See how the economic value of the data can be enhanced. Do you need to increase the accuracy of it or supplement it? Do you need to consolidate that data?
- Identify how you can keep that information consistent, accurate and up-to-date, across those different processes. Do you need to move it externally, then create links back to that data, or do you need to potentially replace solutions that has a better understanding of sharing data across multiple different applications?
- Put in a feedback mechanism into that data asset to see what’s working and what’s not. When does that asset mature, and not need ongoing maintenance? It’s important to understand the usable lifespan of that data.
Data has a life cycle and exists beyond the initial engagement. Data management and maintenance needs to be viewed as an ongoing journey; ensuring information is current and updated.
Because information moves between different systems, keeping data current and updated is extremely challenging. But ultimately, the cost of not doing so will be much, much higher.
By John Heaton, CTO of Moneycatcha
Heaton is an IT and banking technology veteran, having worked in various senior roles with Oracle, Heritage Bank and Xstrata Copper.
Connect with him on LinkedIn.
This article was first published by BankingTech