So you’ve decided to start a data department – congrats! In this series of blog posts, I’ll cover all aspects of starting such a department. This post will discuss the challenges and how the head of the data department can manage the process to optimize it.
Some History
The idea behind even having a data unit or department comes from the realization that data assets are the real assets of companies, and are at the core of their strength. These data assets can help a company grow its business, mitigate risks, increase competitiveness, and provide real, personalized value to customers – basically leveraging any aspect of the organization.
This idea also originated from the volume of data, which has increased immensely, and the technology that now allows organizations to handle this volume and even turn it into a virtual goldmine.
Thanks to this insight, many organizations consider their data departments as important, strategic, and impactful. In many cases, the head of this department – the CDO – is a senior executive who has their finger on the company’s pulse. Moreover, a data department isn’t merely technological – it should combine strategy, tactics, and technology. This is the department holding the rope on both ends and bridging between the business and tech sides. Thus, the CDO is able to provide strategic recommendations on the one hand and oversee the development of meaningful technological infrastructures on the other hand.
As I’ll expand on in future posts discussing the structure of data departments, different organizations have different approaches to the placement of this department in the org chart. The same is true for the data department’s responsibilities. Data departments have partially evolved from what was once BI and Big Data units (especially when the BI manager was more connected to the business side than the tech side), while in other organizations it might have evolved from the Analytics unit. In many other cases, this is a completely new department that can be in various placements in the org chart (usually on the business side, but sometimes under information technologies.)
No matter the placement and the history, all data departments usually have one common goal. The goal is to make the most of the organization’s data – internal, external, structured, and unstructured. Turning the data into a strategic asset, using a cross-function, organization-wide perspective. This goal can be met with the resources at hand, within limits like budget.
Since this is an extremely wide definition, with no set strategy or strict plan, the data department can soon find itself lost, and busy with tasks that aren’t aligned with its purpose. An extreme example is the data department finding itself handling reporting or providing basic data analysis.
Moreover, creating a data department is a huge organizational change. This department can often enter areas that were previously handled by other units (even if not optimally), which means it might step on a few toes.
Other department heads may try to define the data department according to their own comfort zone. I’ve heard department heads say things like “You handle data infrastructures and we’ll decide how to use them”, or “Don’t bother with tech, we’ll provide the technology and you can decide how to leverage it for business”. Either way, the CDO should be cautious of this kind of internal labeling that might affect their department.
The CDO should be very smart about entering the organization and stay away from unneeded battles. In other words: Focus on the results, rather than the organizational politics around definitions and responsibilities.
Let’s keep our eyes on the prize: Provide value to the organization by leveraging its data assets. Nobody cared how big your database or how brilliant the algorithm that provided that insight was, or whether you managed 5 or 50 people.
Under pressure, CDOs may lose sight of this purpose. That’s why they must find a way to collaborate across functions, understand the process of creating a new department is long and that you need to deliver a few “wins” in order to get credit and earn trust – play the long game.
It’s all about finding the middle road. In their multi-year planning and while prioritizing projects (which we’ll discuss in later posts), CDOs should quickly find the areas in which they can get the most organizational attention and collaboration, where the complexity is not too high and the potential for people using the data is high.
Rules of Thumb for Change Management:
-
- Don’t let other departments dictate your responsibilities – often, as soon as a data department is established, internal “customers” knock on its door with various requests. This is of course legitimate and sometimes can be met with enthusiasm from the CDO – having found areas of need. However, these requests might not be the areas that should get the organizational focus or may stir the data department away from handling more essential topics. Remember the data department is an elite unit and needs to focus on the most critical areas, which can also generate the most potential.
- Spend time examining all business segments and technology infrastructures, and then create a plan and stick to it – (stay tuned for a special blog post just on this topic.) In most cases, it’s worth getting external help for this stage from experts to craft this plan in a way that will generate the most significant added value.
- Find “customers” who are willing, collaborative, and looking forward to working together – those heads of department who see the data department as a springboard for their own success and can’t wait to dive in.
- Create a healthy working interface with the technological departments – remember technology is the means, not the end. It’s not in your interest to necessarily own the technological infrastructures. You want to define the need, examine the technologies that can provide the outcomes you’re looking for, and be involved in decision-making – but not to the extent of endangering your relationship with the tech departments. It’s in your interest for the tech department to feel like a meaningful part of the process, and for them to feel like you’re helping them adopt innovative tech, cutting-edge solutions, etc.
- Long-term planning, short-term executing – on the one hand we want to have a 5-year plan, and on the other hand – the organization wants to see immediate results. And justifiably so. In most organizations, there are plenty of “low-hanging fruit” that pose opportunities for quick wins without too much investment.
In many organizations, there is quite a robust data body already with innovative technology and advanced implementation capabilities. Find the right area where you can connect these three to a meaningful business challenge – and you have your quick win. - Don’t forget your goal – which is providing business value by using data. You don’t have to scrape data from social media, create a Hadoop infrastructure, analyze unstructured chat data, or bring the most unheard-of data analysis tool. It’s likely you’ll get there while working on business projects, but if your first project is creating a Hadoop infrastructure, you may need to renavigate. If you’ve arrived at the conclusion that you need Hadoop in order to do customer-based marketing using behavioral profiles and the right infrastructure is Hadoop – great. You had a business goal and you created a technological infrastructure to support it (and many future goals as well).
As I’ve learned from my triathlon coach, it doesn’t matter if you’re the fastest runner, the fastest biker or the best swimmer. What matters is how quickly you reach the finish line. If you manage the data department right, even if you’re not the best at its 3 aspects, you’ll reach the finish line and deliver the goods.
On our next blog posts:
- How do I even begin? Creating a data department roadmap that will deliver quick wins while establishing strategic infrastructure to serve you long-term
- Different approaches to data department structures and responsibilities
- Data-driven customer strategy
- Data-based risk management
- Technologies that can be your data department’s secret sauce
- Data monetization – selling data services and products
