Читать книгу Tech Trends in Practice - Бернард Марр, Bernard Marr - Страница 59

It’s also vital you have a data strategy in place. A data strategy helps you remain focused on the data that matters most to your business – as opposed to collecting data on anything and everything, which is rather an expensive way to go about it! With so much data available these days, the trick is to focus on finding the exact, specific pieces of data that will best benefit your organization. A data strategy helps you do just that. With a robust data strategy you can set out how you want to use data in practice, clarify your top data priorities, and chart a clear course to achieving your goals.

Your data strategy must be unique to your business, but, broadly speaking, I’d expect a good data strategy to cover the following points:

 Business needs. To truly add value, data must be driven by specific business needs, which means your data strategy must be driven by your overarching business strategy. Basically, what is your business trying to achieve, and how can data help you achieve those strategic objectives? Here, it’s wise to identify no more than three to five key ways in which data can help the business achieve its strategic goals, answer key business questions, or overcome its main challenges. Then, for each data use, you then identify the following…

 Data requirements. What data do you need to achieve your goals and where will that data come from? Do you, for example, already have the data you need? Do you need to supplement internal company data with externally available data (such as social media data)? If you need to collect new data, how will you go about that?

 Data governance. This is what stops your data becoming a serious liability, and involves considerations such as data quality, data security, privacy, ethics, and transparency. For example, who is responsible for making sure your data is accurate, complete, and up to date? What permissions do you need to secure in order to gather and use the data?

 Technology requirements. In very simple terms, this means looking at your hardware and software needs for collecting data, storing and organizing data, analyzing data, and communicating insights from data.

 Skills and capacity. Do you have the skills to deliver your data needs and, if not, how will you overcome the skills gap? Will you, for example, need to hire new people, or can you partner with external data providers?