Strategic management of data is important but before we dive into strategic data management, let’s understand what strategic management is by defining it and explaining it briefly too. This is because the term strategic management is used often but many times it is not well understood in the business industry and other industries.
If we were to break both words down then we would define strategies are the initiatives a company takes to maximize its resources and to grow its business. This might involve financial planning, human resources management or focusing on a mission statement. And then, management is the process of operating the business on a day-to-day basis and planning for future success. When you put the two words together, strategic management is activities carried out that are focused on driving the company’s growth through effective management techniques focused on goal-setting.
Strategic management is vitally important even on the small scale within a business. However, strategic management is difficult to accomplish without a clearly defined set of goals for the business’ operation. Knowing what your core competencies are is good from the standpoint of understanding your strengths in the marketplace, but this also helps you to identify areas for improvement and set goals and objectives based on those weaknesses. If you know, for instance, that your business is lagging behind in utilizing the power of the Internet to sell its products, one of your goals can be to introduce an online trading platform within the next six months and to employ online marketing strategies to get that online platform the attention it needs. To employ a strategy of an online trading platform without a follow-up marketing strategy will be bad strategic management and will lead to a failure of the overall strategy. Importantly, the goals your business sets should be measurable, specific and have a time frame attached to them. Setting goals in this way help you to strategically position your business for future success.
Now we’ve briefly discussed the importance of strategic management in business let’s talk about Strategic Data Management and its importance in every industry particularly the healthcare industry. The healthcare industry cannot function without proper data that is relevant and accessible and this data cannot be these things if it is not properly managed.
Strategic Data management can be defined as the development and execution of policies, practices, architectures, and procedures to properly manage full data lifecycle needs of an enterprise. Strategic data management is essentially important in the healthcare industry because of the magnitude of data that it produces and needs daily. Every area of the healthcare industry relies on an endless stream of data flowing in order for the system to function. Data management evolved with the advent of technology which ushered in electronic records. Data previously was stored in paper records, files, and boxes which made it almost impossible to manage. Issues like missing document, misfiling of a document or illegible handwritings served as a hindrance to data scientists who could use data from patient history to predict the future of patient care.
In order for strategic data management to be effective, there has to be good quality data to start with. One of the single most important functions of healthcare data governance is ensuring the data quality. When the quality of data is low, it’ll have an impact on accuracy and/or timeliness of the organization’s decision making. A Data Governance Committee must be set in place to quickly react to low-quality issues and enforce changes required in source data systems and workflows. These sources systems and workflows are the ones necessary for raising data quality. The Data Governance Committee must make sure that these variables (completeness, validity, and timeliness) in the data quality equation a leadership priority.
In conclusion, Strategic Data Management helps ensure data quality and simply defined, data quality is equal to the Completeness of Data times Validity of Data times Timeliness of Data.