All of the information in a company is necessary or useful to a functional organisation and much of it is necessary to support a competitive marketplace. But primarily what is information, whether data-driven strategy material, financial, or emotional? How will you capture it, what types of solutions are available, and what goals are needed to make the right decisions?
Complex organisational databases are designed to support and coordinate all of the above. They aren't designed to hold the right information (or information you want) in the right way. Generally, these organisational structures are not designed for data exploration. Information is stored in databases in some logical way, such as process or person, attribute, or focus. Typically, the databases don't support the complex data-driven strategy analysis of information used to make informed business decisions.
By definition, you can't use an existing system to store data when you make decisions that directly affect that performance or productivity. Data naturally flows among the resources that are currently used; in order to understand the data yourself, you need an understanding of various processes to make the right choice on which to base your decision. This decision must have some degree of control; you need to determine the extent of that control based on the data you already have available. This is not always straightforward, especially if your company has been databases-intensive in the past. The collaboration of data and information is the main data-driven strategy challenge and the area of focus for cross-functional information analysis.
The most important element in this computer age is data transparency. Whether you have a new addition or are increasing your information flow by purchasing new data sources, you need to have the ability to be able to view, analyse, and seize data from different operating systems. This is critical for insight, future planning, and profit improvement. Contrary to the hard-line, material, financial, and operational goals of all the information your company uses, information is only useful when it is used. If information should be centralised, it must immediately convert the data it holds into knowledge and must immediately be able to be used to make data-driven strategy decisions.
Now that you have decided that you are going to make a decision on whether you are going to collect data in the organisations, you need to consider developing the strategy to assist you in facilitating this data-driven decision. The following five specifics illustrate how to begin the process. Each detail implies a need to collect new data-driven strategy information in order to make the decision as your organisation changes and incorporates new processes. This means creating new data channels, data repositories, and the infrastructure to support them.
You have an idea who and what you are. You know how you must obtain data about the people and organisations you are considering as potential data-driven strategy clients, referral and community sources.
You need to identify what knowledge and how that knowledge is stored that you're using to make decisions.
You need to identify if your existing data process and the data repositories in this data warehouse are facilitating the tracking of people and information in your organisation.
You need to consider time frames within which you will need to implement the investment you just determined was necessary in the use of your new data.
You need to consider systems that can provide you with the tools and support needed for detailed analysis of this data, including data modelling and visualisation.
You need to consider how this data model and its data warehouse fits into your organisation.
Of course, you are not necessarily going to gather data in a specific way, you are likely to decide in which way you will gather new information. You can also change your strategy and start from a different aspect of your data-driven strategy organisation to gain a new perspective and before you begin describing a data-driven model. This means including data in other parts of your organisation outside of data warehouses to increase your ability to understand your results.