Next level analytics competency – part two

There are two aspects to analytics: technical and capabilities. Firstly, you need to be able to get the data and visualise it – this is the technical side of analytics. You then need to know what to look for – this is the capabilities aspect.

Before we can understand the capabilities side of analytics, we need to grasp the technical aspect. Today, there is a whole host of Massively Open Online Courses (MOOCs) available to the general public, including EDX, Corsera and BigDataUniversity. These MOOCs provide a wide variety of free and paid-for courses that can assist people in developing their talent and enabling them to reach their analytics capability. Another option is to pick up some technologies and give them a try. Most new technologies for data analytics are very intuitive and do not require a massive amount of technical ability to get out a proof of concept. For example, NodeXL is a great Excel plugin that you can use to quickly get out social media statistics, while Tableau and Qliksense both provide a useful desktop platform to connect to data sources and quickly create a data discovery dashboard. IBM’s Watson is also an option. If you want a lightweight cloud version that you can throw some data at and get some great insights, it is quite affordable.

So who do we hire in the new world of analytics? The technologies we are using are far too young for someone to be an expert in them. When I’m hiring people for a relatively new technology stack, I like to look at the person’s online profile: does he or she have a blog that is respected within the community; has he or she published articles; what is his or her status on forums? In the interview, I try to gauge the applicant’s passion for data and analytics. Because the technologies are so young, I look for a pseudo track record that will provide confidence in abilities and not necessarily a commercial track record.

When you are looking to outsource certain components of your analytics strategy, use the same methodology for the service provider’s staff. Public profile, transparent training programmes and track records are key when it comes to bleeding-edge technologies.

In terms of developing people in the realm of analytics, not the technical side, there is no hard-and- fast method for this. My best advice is to read as many industry case studies as possible, keep up to date with global trends within your sector, and try to use some of this learning to apply internally. Many people outside of your organisation are investing a lot of money into these case studies and it’s an easy win. I also try to read relevant research papers from Masters and PhD students from around the world.

The one point I must stress is that locally relevant domain knowledge is crucial. Understanding the industry and business is a critical component to achieving analytics success. I have mentioned this before but I always look for the ‘Miss Congenialities’ of organisations. These people have been at the company for many years, worked in several departments and are likely to have more domain knowledge than most. Always try to leverage these people when going down your analytics journey.

From a capabilities point of view, there are two additional key points organisations need to take into account. Firstly, the relationships with external parties, which include vendors, suppliers and universities. These people are paid to keep up to date with industry trends, research what the next big thing is and are more than willing to share that knowledge.
Universities in particular are great channels for assisting with analytics capabilities because their research departments are on the cutting edge of what is coming next. They are also in need of corporate sponsorship. Many graduates are doing great work and are worth talking to about ideas you may have to take your organisation to the next level.

The second point is to ‘invest time in wasting time’. This is one of the most important things you can do to keep yourself abreast of what is going on in the world of data analytics. Gather a team and sit and watch YouTube videos for an hour or two on relevant technologies/topics, spend time reading the case studies and just talking about possibilities and ideas.

In the third and final part of this series, I will look at the best case structure for a high performing analytics strategy.

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