What Do I Need to Know about Data for My Job?

Here are some pointers on how a non-data specialist can become better at working with data.

Data has crept up on the workforce. Decades ago, data was practically irrelevant for everyone, and even technical people did not pay much attention to it. That has changed completely, and now data matters in all kinds of ways. Because of this, employers expect employees to be able to work effectively with data, but most organizations provide little training or education about data other than what is required for compliance reasons.

In this article I will try to provide some pointers on how a non-data specialist can become better at working with data.

Competencies

Before we start on the specifics it is important to understand that we are talking about “competencies”. A competency is an attribute that enables someone to do a job well. In general, there are three kinds of competency:

• Skills: Technical abilities to do a particular kind of work. E.g. ability to manipulate

  data in Excel.

• Knowledge: Having factual information about the specific work at hand, and

  understanding this information. E.g. understanding how data about suppliers flows through the organization.

• Personal Traits: These break down into innate or learned abilities, and also

  behavioral characteristics. E.g. being detail oriented, and being interested in

  catching mistakes.

We will examine all of these to appreciate how someone can work better with data. But before we do that, what do we mean by “working with data”? Basically, there are two kinds of ways non-data-technical staff may interact with data:

(a) Indirect Data Management: This is where staff have to update, read, and analyze data as part of a business function. E.g. a nurse who enters medical information about a patient. By far this is how most employees outside of IT and technical data units interact with data.

(b) Direct Data Management: This is where staff are looking after data for the enterprise. E.g. someone who sets up customer records as part of customer onboarding. These are non-technical jobs, but are wholly concerned with data, and not the use of the data.

Obviously, Direct Data Management is going to be more demanding in terms of data- related competencies than Indirect Data Management, but the latter is still very important because there are high risks of creating data errors.

We can think of a scale of competency needs. Perhaps on the lower end we have someone doing repetitive data entry, and on the other extreme someone involved in analyzing data to make business decisions for the enterprise. Even so, across this entire spectrum there are still competency needs. Let’s look at three basic ones in each of the competency categories we have described.

Personal Traits

The following personal traits are important for data management:

Detail Oriented: Data values are designed to be atomic, and as such exist at a low level of detail. Anyone dealing with data must be able to deal with a lot of detail and focus on individual items within a mass of detail.

Orderly: It is very easy to create a mess with data. Just the placement of files in folders seems to be a daunting task in most organizations. It takes a considerable effort to establish order, but it is very important with data.

Abstraction: The ability to conceive of something and manipulate it mentally. Data is non-material, so understanding it is less easy than physical things. Data professionals often say there are “data people” and those who cannot “do data”, meaning those who can and cannot think abstractly. For everyone who works with data – not just professionals – the ability to think abstractly is important.

Skills

The skills around data are technical skills. There are perhaps not so many employees who need these skills, but some roles certainly do.

SQL: Like it or not, SQL has become the universal language of data retrieval from databases. If business staff need to retrieve data and do not know SQL, then they will have to rely on technical staff to do it for them, which may never happen. AI may be helpful here, but the prompts have to be exact.

Excel: If SQL retrieves data, then Excel is used to manipulate it. Most organizations run on Excel, even if IT departments do not like this. Many careers have been greatly enhanced for people who know Excel.

Communication: While in some ways this is a personal trait, it is also a skill that can be learned. Communicating about data requires clarity and precision. It is too easy to create confusion about data with sloppy written or verbal content.

Knowledge

Data Definitions: Staff need to understand what the data they are working with means. This is not just what we think of as a definition, like what we see in a dictionary. Rather it is more like a wiki entry. To understand data, it is necessary to know what populations of things are covered, what quality issues exist in it, the methodology by how it was gathered, and so on. Now, this is not needed for all data, but some of it is needed for some data. Assumptions about data can cause huge problems if they turn out not to be justified.

Permitted Usage of Data: Long ago it was assumed that any data within an enterprise could be used for any purpose. Today we need to know what is allowed. Data Privacy has been the biggest driver of this, but even today many people still think that just having access to data means they have permission to do whatever they like. Besides Data Privacy there are also contractual obligations that apply to licensed data purchased from data vendors. Knowledge about what can and cannot be done with data is very important.

Data Sources and Structures: Knowledge about what data exists where in the organization and how it is structured can be very useful. This is a kind of situational awareness for someone who works with data, and can prevent surprises as well as suggest opportunities.

Gaining Competencies

We have only looked at basic competencies and there are many more. And of course there are many other specific competencies that are necessary for specialized roles that deal with data.

What is much more important is how an average person can be expected to acquire these competencies. In theory it would be to the advantage of every enterprise to help grow these competencies in as many employees as possible. However, this does not seem to be something that is widespread, which is unfortunate.

This leaves it up to the individual. The skills competencies are fully transferable, as are the personal traits, but the knowledge competencies are very much tied to the enterprise and not transferable for the most part. So individuals can be self-motivated to acquire data skills, but less so when it comes to knowledge. However, some individuals do choose to acquire knowledge about the enterprise they work for and become very valuable to the enterprise as a result.

This situation is not perfect and for the individual there are tradeoffs when it comes to acquiring competencies. That said, data competencies are increasingly needed in the modern economy and should be something every employee thinks about for their own situation.