I have worked with lots of companies over the years, either directly as an employee or indirectly as a client, and one thing that has become very apparent to me is that size really does matter. An organization’s size, measured in any number of different ways, will have a tremendous influence on everything about it. From the number of mandated training sessions you need to attend, to the amount of capital that is invested in an initiative, and organization’s size will drive it’s structure, its processes, and its culture. Certainly it isn’t just size that influences the organization, obviously, since size is really just a byproduct of a company’s strategy. But the size of the organization will be a key consideration if you are seeking to influence that organization’s data journey, because much of what we have to do to be successful in this space is drive change on several levels. This is because the data maturity of an organization does not necessarily correlate with a company’s size.
The Fallacy of Excellence
Let’s take a fictitious retail organization, Grayson Designs. Inc. It has been successful in it’s mission. It is leader in the home decor market, they have thousands of employees, and have enviable profits year over year. It is reasonable to assume that their capability within one core area of their operation, would be seen throughout their organization to some extent. In other words, if product design is a world class competency, then their technology and logistics operations couldn’t be completely broken. After all, their track record of success is clear and consistent – how else would they achieve their results, of which they are so rightfully proud? This is a reasonable thought process, but in reality it isn’t necessarily true. Every organization I have worked with and interviewed has a disparity of success across all of its domains. One area, hopefully the one that is most central to their success, may be high performing; while others might range from fair to “just plain awful”. As organizations grow this can become even more pronounced. While Grayson Designs has a remarkable advantage in how it connects with its customer segments, creating products that truly resonate with their audience, their manufacturing may only be average. As they grow, and expand their manufacturing operations to meet growing demand, it is reasonable to expect its manufacturing operations to go from “average” to “poor”. This could simply be due to scale. Scale has the potential to amplify outcomes, both good and bad, so in this case average gets worse.
While the example is fictitious, it is based on real forces in business and there are many organizations that experience this phenomena. As organization’s grow and change to meet the demands of their customers and industry, they can continue to achieve success, while failing to optimize that success. We can see this in the data we work with every day. In order to meet a systems implementation deadline, we cut back on how much data is converted. We acquire a competitor, without fully integrating their systems making our data environment twice as complicated, and many times less valuable. We see a reduction in force as a cost cutting measure, but fail to incorporate the departing institutional knowledge into our meta data management. Anyone reading this can likely relate to these scenarios because they happen all the time. Even at successful organization who may be excellent, without being excellent at everything they do.
The Fallacy of Organization
When I hear the term “organization” one of two things pops into my mind. Either the idea of chaos being whipped into submission and everything being just as it should be, neat and tidy. Or a big monolithic entity filled with people, entrapped by processes, mindlessly filling out paperwork. Now I am not a psychologist (and I probably do need one), but I find it strange that these two very different images exist for the same word in my mind. I also know that I actually have seen one, much more frequently than the other in the real world. Organizations, as they grow are almost certainly going to become more bureaucratic, despite even the most diligent attempts to avoid it. I was introduced to the “Rule of 3 and 10” a philosophy of Hiroshi Mikitani, by a former CEO of Evernote, Phil Labin. This is the theory that an organization’s processes and procedures will tend to break down after every 3rd and 10th steps. So if your organization scales from 100 people to 300 people, you can expect problems to arise, and likely even more so as you move from 300 to 3000 people. The proactive, thoughtful re-imagining of new processes is seldom a priority. In fact, unless there is a burning issue that is inhibiting your central mission, this seldom happens at all (that is why consultants exist). So instead, band-aids and work-arounds hold once efficient processes together. Ill-fitted procedures move slowly and miss their mark, leaving the intended outcomes far from realized. All of this resulting in your organization getting less organized over time.
Data leaders in these situations can expect to find real challenges to their agenda. This is because processes tend to mirror systems, and systems dictate what your data environment will look like. Convoluted processes, can lead to multiple systems, and silos of data. Ad hoc solutions, lead to manual processes, circumvented systems, and lost data. Fragmented operations that have failed to scale require greater investment to correct, making progress more challenging politically. All of these things will hinder any progress along a data journey, limiting the value and ROI you can expect from your decision science investment.
The Fallacy of “People are our Greatest Asset”
I have heard, more than once, leaders tout their people as their “greatest asset”. If they really mean it, then I short those companies. People can be a huge asset, they are a source or creativity and passion, and can be the difference between an average company, and a stellar company. But they can also be liabilities.
Processes aren’t the only things that change as an organization grows. People come and go, and not always the way you would hope. New views and attitudes influence the culture of your organization and the leadership. This can cut both ways, but one thing that you can be certain of, is that new motivations and agenda will also be a byproduct of this change. Operational structures may change, distributing authority differently or across a broader population. Responsibilities may shift, changing how your organization runs itself. All of these things act to increase the complexity of influencing change.
The people you consider your internal customers, champions, or allies can be influenced by these changes directly, changing how they engage with others or altering their behavior even more
remarkably. Data leaders have to constantly manage their relations with customers, peers and more senior executives. They may find that they will have to become more “salespeople” than “analysts”. They may have to spend their time building relationships instead of models or even their teams! As power shifts within an organization it is crucial to be involved and active in managing the your data mission, its communication, and it’s connection with business leaders.
Big or Small, Isn’t Good or Bad.
There are plenty of large organizations that are amazingly well run, focused on maintaining a strong and positive culture, driving efficiency and using data to make decisions. There is just no guarantee that they will always be that way. Same can be said for their opposite. Your takeaway should be that you can’t ever lose site of the current state of your organization’s people, processes and priorities. Considering your company’s size and its growth periodically can help you take stock and evaluate what may need to change in the data side of its business, in order to achieve the goals that have been set out for you.
To help you consider this, we have developed a model that you can use to perform this periodic evaluation. The Authority-Activity Array (ooo…ahh…) plots organizations on a field based on how diverse their operations are (their Activities) and the diversity of their decision rights (Authority). Based on where an organization falls, data leaders should consider different strategies as they drive their data journey. For example, if decision rights are not diversely granted, or they are held by relatively few persons, then their engagement model should focus aligning data strategy with those few key decision makers, in general. However, if there is broad diversity in the activities of the organization (and scope of control for the data organization) then the data leader may have to employ a multi-tiered engagement model to ensure alignment with the decision maker’s strategy. The point is, that Authority and Activities are going to be distributed along an array of possibilities and change as an organization grows.
This isn’t an exact science, so don’t think of this as formulaic. Instead, be aware and attuned to your organization. Seek to understand where you are in your evolution and where you think you will be in the near, medium and long term. Recognize that size and growth will create new challenges for your data and analytic strategies as you lead your organization along it’s data journey. Lastly be proactive, and anticipate where you expect to be, and those challenges that you think will emerge – and plan tackle them head on, no matter what their size.