Нужна ли стратегия работы с данными в компании? Безусловно. Кто это будет делать? Директор по аналитике (Chief Analytics Officer) или директор по данным (Chief Data Officer)? Зависит от структуры компании и её общей стратегии. А вот несколько советов о построении стратегии работы с данными.
So there’s a lot of data out there. Now what? How is that going to make a difference to you? That depends on what you want it to do, how many resources you have to devote, and how much effort you want to put into maintaining your data driven approach over the long term. Make no mistake. There are a lot of different options for solutions out there, some more human intensive than others. But only some will transform the data that is important to you and turn it into relevant and insightful information you can use. To get there, you should start with a great data strategy.
Any good data strategy is going to involve understanding key factors that will impact implementation. Some of it will be obvious and based on what you should expect in any data strategy piece. For instance, there should at minimum be some sense of requirements, how the pieces will work together, how data flows will be managed and visualized, and how different users will interact with data over time. But if the process is really good, it will also explore the burgeoning potential of new data driven approaches and help you determine which are right for your approach. To get to great, consider the following:
1. Brainstorming on Current and Future Goals
You would be surprised by how many people forget to figure out what they really want their approach to achieve. Many hold minimal discussions with organizations about capturing requirements from the immediate mission and move straight to designing implementation schema without fully exploring what is possible. But a great strategy will involve asking the right questions to get you beyond what you know and into the realm of “Hmm… I didn’t think about that.” It will get you thinking about how to use data now and how you could use it years from now. It’s only once you understand the potential goals and possibilities that you can narrow down your options into something realistic that will meet expectations and build for the future.
2. Understanding your End-to-End Business Processes
Yeah. This one is pretty obvious. But for a data driven solution, some strategists will just look at business processes related strictly to data flows. To design a great approach, a strategist will look beyond immediate data flows and seek to understand how your organization’s broader goals, and current and future process could potentially be impacted by data, how users work with data now, and how they might work with data in the future. Indeed, a really good exchange will get into the details of process and reveal the possibilities of where the right data approach can provide multiple levels of insight, and help your key personnel save time and resources over the long term.
3. Doing a Data Inventory
One of the great opportunities data driven approaches provide is the ability for organizations to look at the data they currently have and explore how it can be augmented by the ever growing availability of new data sets and streams. Some of that new data might emerge from the implementation of new tools or techniques that enable you to mine untapped sources within your organization. Others might be well structured feeds provided by vendors, structured data coming from the ever expanding web of inter-connected things (the Internet of Things), semi- or unstructured data coming from indexed sources on the internet, or even data emerging from alternate, non-obvious sources that exist but have not been fully integrated into any grid (aka, the “wild”). A good data inventory will get the basics of what you have, but a great data inventory will also help you understand what else is out there that is relevant, and what will be available soon.
4. Knowing which Tools and Techniques to Use
With hundreds of different tools and techniques available, knowing what the strengths and challenges of different approaches are and what will work for you is critical. It’s not always obvious which tool is right for you. Some techniques may be more than what you need. Some, not enough. Indeed, when it comes down to your architecture and design, your strategy will need to explain how these components will work together, identify potential issues that can emerge, and provide workarounds that will help your approach succeed. Because a good strategy will be designed based on how the tools should work, but a great strategy will reflect the experience of what actually does.
5. Legal and Policy Considerations
It’s a given that data governance and policy for most industries is still largely unregulated. There are plenty of good strategies that have and can be developed with little regard to outside influences, but that’s changing. In the era of increasing data breaches, cyber-attacks, calls for regulation, and lawsuits, it is inevitable that outside influences will affect your approach. A good strategy could be developed with minimal consideration of external factors, particularly in industries where little to no regulation exists. But a great strategy is developed with foresight into which external factors are most likely to affect your approach, the associated level of risk of each, and flexible contingency plans that can help maximize benefits and minimize negative impacts.
6. How your Approach will Work in Different Locations
We’re going mobile and we’re going global. And we are increasingly finding ourselves deploying approaches to collect and analyze data in multiple, cross-cultural operational environments. It’s no longer enough to develop good strategies that look at approaches with a single “global” or “national” standard. With mobility and accessibility to data from multiple devices as the new normal, the strategies we develop for our data approaches must enable us to adapt to different, increasingly localized environments. That means we have to understand the differences of infrastructural capacity, geophysical climates, bandwidth, accessibility, and user usage norms that can exist at the local level. It can also mean understanding what is local from a local’s perspective and how that will affect the implementation of your approach, whether that locale is a small farming village in Sri Lanka, a manufacturing zone in a Baltic State, an elephant preserve in Kenya, or a technology firm in a small town in North Carolina.
7. How your Target Market will Interact with your Data
It used to be that we could design interactive experiences based on assumptions of how users have interacted with data in the past. But data driven approaches are just starting to enable the exploration of what data can do and how users can interact with it. There are very few, if any, use cases of direct relevance to most organizations or industries from which to look for inspiration. As a result, developing interactive experiences for use within a great data driven strategy means more than just research into user preferences and knowing what’s already been done within your industry. It also requires knowing what has been done in other industries, and in other related and unrelated sectors. Inspiration can come from anywhere, and a great strategist will recognize when it is relevant to your approach.
This is all to say, it’s worth it to invest resources in developing a data strategy before you start implementing and testing any approach, whether that strategy is good or great. Investing a smaller sum at the onset of any data driven approach will help to assure that the system you ultimately implement works the way you want it to, avoiding costly modifications in the future.
But if you’re going to invest in a data strategy, it’s worth it to spend a bit more and get a great one. Why? Because a good data strategy will help ensure that the approach you choose will work within your current business processes and grow with you in the short-, medium-, and long-term. But a great strategy? That’s what will let you see what is and is not possible, and ultimately help your data tell the stories that really matter to your bottom line.