Getting started in Data analytics

First are senior leaders. Typically they will not be technical so communications need to be in simple jargon free language and focused on the business deliverables. You will likely need to provide some executive education of what analytics actually is and what business benefits it can deliver before you can even have a conversation about analytics projects.

Next is the middle management level. You should work to raise their awareness of how analytics can help them do their jobs and assuage their fears of being monitored or replaced. Using the term Augmented Intelligence rather than Artificial Intelligence is helpful and probably more accurate. Look for people already working in some areas of analytics in your departments and support them and turn them into champions for analytics projects.

Finally there is the IT Department. Analytics projects are often sponsored by business units or analytics teams and can be very different from IT projects. IT projects typically have clear requirements and clear deliverables and so, can be made into a defined project fairly easily. In contrast Analytics problems may be high level with unclear requirements and deliverables requiring a lot of research and testing, all of which require a more flexible timeline. For this reason IT projects and Analytics projects need to be handled differently. The important thing is to ensure there is clarity on how these different types of projects are handled.

Data

Last, but by no means least, is data.

In the past year I have spoken to many data scientists and all agree on one thing—that more than 50 percent of the work effort is spent in data cleansing and data quality activities.

Historically in corporate environments the focus is on individual systems but analytics increasingly requires data from multiple systems—internal and external. Once you start to work with data from multiple systems you typically find inconsistent security access issues and that no one has responsibility for data quality.

Data standards, data governance and data lifecycle may be unfamiliar terms in some organisations. The more you do analytics the more you will become familiar with them.

Do not underestimate the amount of work effort required to collect, cleanse and quality check your data in order for it to be a solid foundation for your analytics efforts.

These are some important topics to think about as you get into analytics, and topics you should think about before you think about technologies.