The Chief Data Officer: Heroes of Data
It’s been about decade or so since data came to the fore, as one of the assets that companies need to focus on. The journey has been quite an interesting one. I’ve decided to put together the journey in my own words and experiences. I’m not going to go back to the legacy application journey, I think that will just be far too long an article!
The journey almost inevitably started out with companies looking to implement some form of reporting system and depending on IT to implement these platforms. It wasn’t such a great job that IT did, as many components were from different vendors, making the stack very cumbersome, expensive, and difficult to operate. At some point the business woke up and realised that IT wasn’t doing a good job with managing the new corporate asset. The business then took over from IT and fell into a similar trap of not laying the foundations for data to thrive. Namely, the right data strategies, embedding good data governance, ensuring definitions across the business were agreed, understanding why data models were required, and changing the culture within the company to understand the value that data brings with it. Because, there wasn’t a single person responsible for data across the business, departments invested in different systems, and thus the fractured and siloed pools of data were extended. IT and the business both failed in my opinion.
Each system came with some form of reporting; however, this was static in nature, and it manifested itself with a plethora of excel driven reports which had errors, were out of date and out of sync – not telling the true story of data. Business intelligence platforms started to gain popularity and IT got into the mix again, because the business didn’t understand the technicalities required. IT stepped in and the CIO or CTO took on the mantel of data once more. The CEO wasn’t anywhere to be seen in the equation and lacked input into the process. Slowly, over time vendors were updating their technology stacks to “do everything”, which was clearly not the case and companies were buying different platforms, tools and overdoing it on technologies. Confusion set in, as the business started to want more from data and IT couldn’t deliver. Data had spiralled out of control nit being governed correctly, but no one wanted to touch data quality as it was an expensive cause. Culture across organisations hadn’t shifted to a more data focused one, and therefore, no one knew how and why data would be crucial to their jobs / departments.
The CMO was probably the first to think about how to use data to impact marketing and sales, however, processes and people weren’t in place between the marketing and sales department, so they both lacked the ability to understand one or the other. Hard pressed the CMO pushed forward to understand what could be done with data and found that they too had issues with quality, data integration, definitions etc. So, the same issues existed, and no one knew how to tackle them. The CMO didn’t realise how many systems they were using both internally and externally that generated data, but to connect them together was a big job, budget wasn’t available for doing this, and the CMO didn’t know how to broach it at an executive level (well only a few had the real strategic understanding).
There was much annoyance with data, and everyone was looking for the saving grace. Along came the data scientist, a unicorn that was going to solve everyone’s problems. Droves of so called data scientists came in, and traditional BI teams had to work within a new equation, who were they and how were they going to fit in within the new structure. No ground rules were laid down, they were the new frontier and were going to wow everyone in the organisation. Wrong! These were deeply technical folks, with little understanding of the business and in some cases, from my experience egos the size of football pitches (the English game). Fundamentally, they all had the same issues, the quality of data was the main struggle, cleansing, de-duping, normalising or not, architecture didn’t support the huge amounts of data being generated etc. So, we began collecting systems again – Hadoop became a fancy place to store data – costly and not useful when you want to scale and offer quick analysis to your internal customers.
Chief Data Officer 1.0
Then a wonderful thing happened probably about 4 – 5 years ago (or maybe 6). The UK is always behind the US curve when it comes to technology, because we just are. The Chief Data Officer (CDO) was heralded as the person who was going to break the cycle of data spiralling out of control, put data back into the hands of the business, and create the platforms that were required to bring it all together. Well, CDO 1.0 was the person that didn’t’ have a mandate, bought in and reporting to the one of the C-Suite: CTO / CIO / COO or CFO didn’t help. The CEO still not in the frame with data and ignoring the real potency that utilising data effectively would offer the business.
So, CDO 1.0 had an uphill struggle. The main task wasn’t building the value of data, it was educating the business on why data would help them. By now though, the business was extremely sceptical. They had been bamboozled by the number of systems that were in place, the number of big consulting firms revolving through the front door stating that they would be able to lead them into utopia. At the same time, the numbers of technology vendors dropping in systems such as on-premise, cloud, SaaS etc., was all too confusing and there was no real uptake, as the business weren’t involved in “IT projects”. Plus, the culture hadn’t adapted or changed at all and things were still as they were.
CDO 1.0 had to counter all of this, can you imagine having to pick up all the pieces, and attempt to change things with little support from the CEO or other C-Suite execs, even if you have a “C” level in your title. The struggle continued, and the only thing CDO 1.0 could do was education, education and education. Tough to do when you are removed from the person at the top, and the tip of the iceberg has been laden with all the technology in the world, but the foundational capabilities haven’t been formed. The struggle for CDO 1.0 was to get the investment to lay the foundations, as the person they reported up to queried why there was a need when all the technology was in place. Secondly, data strategy wasn’t something CDO 1.0 could get far above their reporting line, as typically they were at the behest of the CFO / CIO / CTO / COO, and often they were the ones directing the strategy towards bits and bobs that would help their cause and not the enterprise.
The handcuffs were too tight, and CDO 1.0 was attempting to explain the value of data, garner support for the foundational capabilties that were required (which were always overlooked), and the explosion of the buzzword “big data” hindered their cause even further and muddied the waters to an extreme extent. The struggle was too great for CDO 1.0 to get the show on the road, but, not all was lost, 1.0 laid the foundation for 2.0.
Chief Data Officer 2.0
Step in CDO 2.0. By this time, CEOs were slowly beginning to understand that there was this thing called data, and that it needed to be treated specially. Still not understanding the why behind it, as there was very little value to show for data. CDO 2.0 was able to leverage the education that 1.0 had espoused, and very quickly located the low hanging fruit. In most instances, this was marketing. 2.0 was able get to the marketing data, and start to do simple customer segmentations, analyse campaign effectiveness and ensure that marketing and sales were closely integrated with data. Still, there were the legacy issues that existed, the foundational capabilities weren’t in place which caused issues with reporting (as it was called then). 2.0 also started to climb up the corporate ladder, as the CFO / CIO / CTO were too busy with their own workload and focus, data was a distraction.
It was at this point, data strategy started to gain traction. 2.0 was able to cavass the enterprise, talk to key stakeholders about their issues, start to uncover the foundational activities that were required. Starting to think about the data organisation, and how it should be structured i.e. a business intelligence competency centre or an analytics centre of excellence. Where were the people that needed to be swept up from across the organisation? 2.0 began to think about consolidation of data architectures and tools, and most importantly was able to experiment with data. Taking small use cases across the business, using a handful of people (cobbled together) from developers, data scientists, integration specialists and business folks, quickly getting data together to demonstrate value. This worked to the extent where it piqued the interest of the CEO, finally!
The CEO was also beginning to read and hear about how “data-driven” companies such as Amazon, Facebook, Google etc. were using data to grow their businesses. This was somewhat of a breakthrough for the CDO, as this is was the recognition that was required from the CEO to put data as a vital component of the boardroom. The CDO had come a long way, however, there was still some way to go. Data had now been lifted to the realms of the CEO, it still didn’t have an enterprise-wide strategy that was required. It was at this time, the CEO decided that the CDO position needed to be reporting directly to the her. No “C” level executive in the middle to muddy the waters, but a direct link to the organisational strategy. Bingo, we’re in!
Chief Data Officer 3.0
That’s when CDO 3.0 was born. It’s at this point, I think I need to lay down the reach of 3.0, and their remit. Let’s take a look from a business perspective why 3.0 has a different view of the organisation from 1.0 & 2.0 – whom we should thank profusely for laying the foundations and fighting the good fight.
CDO 3.0 Takes Data to the Business Strategy
3.0 is here and here to stay. They have been able to leverage all the good work done by previous versions. I now think it’s time to talk about how the CDO role moves forward and aligns with the business strategy. I’m going to lay out the remit that they need and how they need to take it forward. But, first let’s look at the focus of the CEO. Generally, the CEO is focused on three areas:
- Customer experience
- Gaining operational efficiencies
Which sums up what most companies are attempting to achieve. CDO 3.0 sits at the core of these three areas, and is expertly involved in creating the data strategies that ensure the delivery of those strategic objectives. Each will come with goals and objectives, which 3.0 will hinge the data strategy on. But wait, what about all the foundational activities that 1.0 and 2.0 couldn’t achieve. Well, with the CEO embracing the CDO into the boardroom, there is now more education that needs to be done at that level, which makes it easier for the CEO to see the issues that have mounted up over the last 10 or so years.
Putting this in place, it leads me onto the list of those foundational activities that CDO 3.0, needs the CEO to get behind. Namely:
- Data Strategy
- Data Architecture
- Data Management
- Data Governance
- Data Innovation
These are crucial for any organisation to ensure their data initiatives are going to accelerate the CEOs focus areas. Get the foundational pieces in place and not lose ground against the competition.
Now we have taken care of those foundational areas, what are the traits of CDO 3.0? I’ve often heard that people have a “T” shaped role, and if you look back at 1.0 and 2.0, they struggled with the organisation, bringing data to the fore, being business and technical focused, attempting to prove value and so on. Well, 3.0 as they have been acknowledged by the CEO, needs to look at the whole enterprise now, which is the most exciting aspect of the role. The traits of 3.0, and it’s the “T” that helps give 3.0 the breadth and depth across the organisation.
The horizontal bar cuts across the enterprise and has more of a strategic view, mainly due to the seat in the boardroom. 3.0 needs the following qualities:
- Leadership for being data driven
- Change management and communication capabilities
- Must be business centric and understand the business drivers
- Evangelical about data, and it’s uses / value across business lines
- Be able to foster a culture of data sharing and the importance of data quality
The vertical bar provides the CDO with the depth that is required to be analytically minded and focused on translating the business needs into tangible analytics products. 3.0 needs to have the following qualities:
- The addition or consolidation of tools and technologies to support the business drivers
- Be able to optimise data architecture, data models, Master and Reference data
- Organise how the data teams operate and innovate enabling such capabilities as data monetisation, enrichment of data etc.
- Implement solid data governance with strong policies and controls to protect company data
If the CDO gets this right and creates the balance that is business facing and analytically focused, then companies will get to that utopia. But, believe me this isn’t an easy task for 3.0.
There are many organisations out there that haven’t installed a CDO yet. Organisations that have are still struggling with data strategies and the quality of their data, let alone the consolidation of systems. As long as the CDO can raise the awareness of data, build the capabilities, drive innovation, evangelise the vision, collaborate and monetise data, then that is an organisation that will advance at pace, and the CEO will have a very large smile on her face that will outshine the competition. Sadly, I think it’s the converse in most organisations where no CDO exists, there is talk of digital transformation and the Chief Digital Officer becomes the default data person amongst the CIO / CTO – adding more confusion.
The biggest challenges for the CDO now, is to ensure that there is a place at the CEOs table, which puts data on the CEO agenda. The expectations of the CDO leaders of tomorrow is one of AI, machine learning, cognitive computing, natural language processing, neural networks and all the advances that are being discussed now. But, and it’s a big one. If the CDO doesn’t get the foundational capabilities in place it will take longer for the organisation to become data focused, it will cost more bringing the sceptics back into play, and less will be delivered leaving the door open for the CDO to vanish down the food chain and back into obscurity.