The Importance of Data Context and Relevance to Business Processes
Working with data is a complex process that involves many steps and various processes. Whether the end result of data analysis and implementation is a new technological device, a piece of medical equipment, an online portal, or something else entirely, itâ€™s important to consider data relevance and context at every step of the process. Failure to do so can lead to inefficiency, excess costs, and a host of other problems. In some cases, it can end the project at hand altogether. So what are data relevance and data context? And what do they have to do with business processes?
In data as in literature and most other areas of life, context is vital. Paying attention to context simply means looking at the surrounding information and making sure to take that information into consideration when making determinations. In data analysis, then, context could include a lot of things, but weâ€™ll focus on three contextual elements: your research question, the surrounding data, and the existing research. Before we do, letâ€™s discuss the importance of context.
Importance of Context
Context is important because if youâ€™re not taking into consideration the surrounding factors of a particular data set, youâ€™re getting an incomplete picture. Context helps researchers focus on the particular issue at hand and makes it easier to weed out unnecessary or irrelevant information. It also helps to verify findings or, in some cases, can provide a reason for researchers to start again and see what they missed. Nothing exists in a vacuum, which is why context is so important to data analytics and healthcare research.
Research Question as Context
One element of context is your research question. If youâ€™re focusing on a specific problem, like how a particular drug affects children under the age of 12, then every piece of data you collect should help to answer that question. If you start to find interesting information about how that drug affects the elderly, you might be tempted to include it in your data set and analyze it. Of course, that data, interesting as it might be, will likely be unhelpful in answering your research question. In situations like this, referencing your original research question (or project goal in development scenarios) can help you stay focused so you arenâ€™t tempted to follow rabbit trails that will only waste time and resources.
Surrounding Data as Context
Another important contextual element is the surrounding data. In other words, donâ€™t look at every piece of data as its own entity. You have to look at the rest of the data youâ€™ve collected about a particular topic or issue so that the data in question can be accurately judged. Only looking at one piece of data and assuming it exists in a vacuum is like taking a single page out of a book and thinking you can understand the whole story. Make sure youâ€™re looking at all the pages of the book and all the pieces of the puzzle before you make any determinations.
Existing Research as Context
Finally, you want to pay attention to the existing research on the subject at hand. If youâ€™re coming up with results that are completely new, you might be doing something wrong. Again, nothing exists in a vacuum. Even if youâ€™re coming up with innovative technology and cutting-edge treatments, the foundational data you use to develop them shouldnâ€™t be entirely unique from anything that anyone has ever found before. If it is, you probably need to figure out where you went wrong. In this sense, utilizing context can help you avoid serious errors and can save you a lot of time and money.
Data relevance might sound a lot like data context, but itâ€™s slightly different. While looking at relevance is contextual in a sense, relevance tends to be more specific to your goal. As with data context, there are many forms of data relevance. However, weâ€™ll be looking at three main points of relevance: organizational goals, project goals, and customer or end-user needs.
Importance of Relevance
Relevance is important because it helps you keep your project- and organization-specific goals in mind. Itâ€™s possible for a dataset to have great contextual importance but not be relevant to your mission or goal. For instance, to use our previous example, maybe you find a dataset that has to do with how a particular drug affects children under 12, and you think itâ€™s a great fit. Contextually, it is. But then you learn something new. The dataset in question only focused on children in one small population that had other illnesses and youâ€™re only focused on children who have no other illnesses except for the one the drug treats. In this case, the dataset has great context but almost no relevance.
Relevance to Organizational Goals
Business processes are largely united with organizational goals. In fact, almost every business process seeks to accomplish an organizational goal or make the facilitation of that goal easier to fulfill. Any data you collect and analyze in relation to an organizational product or development must be in line with your organizational goals in order to be relevant. In this way, making sure to create clear organizational goals that relate to the project before you even begin collecting data can help you focus your efforts right from the start. The end result should be a cleaner, more relevant data set.
Relevance to Project Goals
The second kind of relevance that you should take into consideration is that of project relevance. Does the data youâ€™re looking at and working with have any relevance to your project goals? Does it answer the research question or help to answer it? Does it provide any value or supporting information, or does it simply add clutter to an already large pile of data? Ensuring that any data you take on board to work with and utilize is relevant to the project goals is essential to creating an efficient and effective workflow.
Relevance to Customer or End User Needs
Finally, the third kind of relevance weâ€™ll look at is relevance to end-user needs. Itâ€™s easy to get caught up in the fascinating collection of data you have before you and take off down research rabbit trails that wonâ€™t help create a better product or service. Even if the data youâ€™re chasing after is contextually important and seems to have relevance to the project, if itâ€™s not helping you create a better product or service for your end-user, itâ€™s not relevant enough to take on board.
Context and Data Relevance in Harmony
When you marry data relevance and context, you create a harmonious system of checks and balances that will help keep you on track. Staying focused on the project, the organization, and the end-user is critical when youâ€™re working with data. It doesnâ€™t matter what youâ€™re developing, relevance and context are essential to business processes in that they help ensure youâ€™re not wasting time or money on investigative avenues that do nothing to further your goals. There are many benefits to paying close attention to data relevance and context. Here are just a few of them.
Whenever youâ€™re developing a product, creating an application, or pursuing an organizational goal, you have to make many decisions along the way. Informed decisions are almost always better decisions. But you have to make sure the information on which youâ€™re basing your decision is relevant and makes sense in context. When you do, you can make far better decisions than you would be able to otherwise.
More Efficient Data Implementation
Efficiency is everything when it comes to implementing the data youâ€™ve collected, analyzed, curated, and stored. Beyond trying to get things done fast, getting things done efficiently means facilitating speed without sacrificing accuracy. Irrelevant data or data that doesnâ€™t make sense in context is clutter and can slow down the entire process. The more agile and on-point your working data is, the more efficiently you can implement that data.
It also makes sense that if youâ€™re using better data, youâ€™ll arrive at more accurate conclusions. No business process ever suffered because of better accuracy. On the contrary, inaccurate data, even if the inaccuracy is seemingly very minor, can cause big problems. This is particularly true where healthcare data is concerned. Data relevance and context are necessary considerations if you want to arrive at a more accurate conclusion. In fact, keeping those two factors in mind can make entire business processes more accurate in and of themselves.
Greater Value to Customers and End-users
At the end of the day, it all comes down to providing the greatest value to customers and end-users. If youâ€™re developing medical equipment, you want it to provide the greatest value to those who benefit from it as possible. If youâ€™re creating a healthcare app, you want it to serve the end-user in a valuable way so that itâ€™s easy to use, makes their life less stressful, and facilitates higher levels of wellness. Even within an organization, developing a new business process that seeks to deliver care more efficiently has the patient in mind. Anything thatâ€™s done in the healthcare industry should seek to increase wellness and quality of life, improve access, or otherwise add value to the lives of patients. Paying attention to data relevance and context in the earliest stages of development and research can help to create more value and higher quality of care to those at the receiving end of your project or process.
Big data is so-named for a reason: thereâ€™s a ton of it. The same volume of data that makes it so useful also makes it challenging to work with. For this reason and others, itâ€™s critical that researchers, developers, and organizations who work with data keep data relevance and context in mind at every stage of the process. Working with highly relevant data that are contextually appropriate will help to create better quality products and services, more efficient processes, and increased quality and value across the board.