The trouble with Big Data: Measuring what matters
Data is undeniably ubiquitous. It underpins every transaction, operation and interaction within Financial Services today.
For the past 10 years we have been on the cusp of the big data enlightenment era, where capturing, managing and gaining business insights from data leads to customer-centric products and performance that sky rockets. So why haven’t Big Banks been able to fully capitalize on this so far, and what direction should they be going in?
The amount of data in existence is growing exponentially – 90% of it generated in the last two years alone1.
In this world of big data, it’s very easy to get lost in all the noise and very difficult to extract materiality. The old adage ‘what gets measured gets managed’ becomes confused because you could measure everything, but you know you can’t manage everything.
It is here that we get to the nub of the question: how do we see the wood for the trees?
Focus on what you need to know
The role of the Chief Data Officer (CDO) has increasingly been embraced as the C-suite’s solution to navigating today’s data-intensive world. They must bring focus and structure to an organisation’s data, and clarity on what insight is required. The first move in the CDO’s playbook should be to determine whether the organisation has the right data to answer the highest-priority business questions.
The first move in the CDO’s playbook should be to determine whether the organisation has the right data to answer the highest-priority business questions.
Often managers will be interested in just a small sub-set of big data, which offers them decision-enabling insights. Rather than collecting masses of data – just because it can – a firm should focus on justifying the need for having it in the first place.
The power of big data lies in how it is used, not the quantity available. Unfortunately, there is no “one size fits all” solution, so a CDO needs to tailor prioritisation based on the challenges, culture and core competencies of each individual business.
Manage the Data Supply Chain
For the data you do need, you can then focus on controlling the data supply chain: from sourcing through to reporting via the key people, processes and systems that can impact the quality of the output. For the CDO to add insight and value, they must recognise where data sources are discovered, ingested, processed, stored, analysed, and ultimately applied across the organisation.
By extending data quality analysis to all parts of the data supply chain it is evident that data is no longer just an IT problem, it is a Business problem. Furthermore, data traceability, lineage and provenance are becoming concepts that the regulators expect to see in any financial institution.
By extending data quality analysis to all parts of the data supply chain we can recognise that data is no longer just an IT problem, it is a Business problem.
Understanding data flows is a complex task, especially for traditional banks with enormous volumes of data. Incumbent banks need to work harder to unlock the insight hidden in customer data, such as transactional data, to deliver better products. Applying consistent methodologies and guiding principles goes some way to offering more confidence in the underlying data and, consequently, fact-based decision making.
Use KPIs effectively
Assuming the CDO is now focussed on the right data and that the quality is reliable, the next task is to turn the data into Management Information (MI) that answers the highest priority business questions, and helps the organisation achieve its goals. To be effective, this MI must be supported by well defined Key Performance Indicators (KPIs).
Goals and KPIs are not the same thing. The goal is the outcome you hope to achieve, whereas a KPI is a metric to let you know how well you are performing towards that goal. This difference must be well understood by management to keep decision making aligned to the strategy of the business and to stop KPIs from turning into targets.
While a lot of commentators call out legacy systems as a major challenge for Big Banks, legacy KPIs are also an issue. By focussing time on what should be measured today, as opposed to what has been tracked historically, companies can start to overcome the data induced self-harm they are currently experiencing. Technology and analytics continue to evolve and so the CDO must continually challenge existing KPIs and bring focus back to the key business questions.
Focussing time on what should be measured today, as opposed to what has been tracked before will enable companies to overcome the data induced self-harm they are currently experiencing.
Reap the rewards of the Big Data revolution
To utilise big data effectively, organisations must understand their data and be clear on how it supports effective and transformational decision making. The role of the CDO is pivotal in this journey and can help an organisation identify and measure what matters in a data centric world.