Why there is minimal adoption in Advanced Analytics in Canada
“Analytics is the future”, “AI will be revolutionary”, “ Data is Wealth”.
It seems like every day I open up my Linkedin or any business news site, there are five new articles that talks about why data, analytics and AI is going to be the future (not just because I subscribe to a lot of data news outlets). As an analytics services company, we have talked to hundreds if not thousands of organizations and most of them are at the beginning stages of analytics if at all in Canada. If they do have analytics or data science functions, they often comes down to 2 or 3 people for the entire company that takes requests for data analysis or report building.
Where are we in terms of analytics in Canada.
Short answer? Behind. From most surveys and talking to industry experts, Canada is significantly behind other advanced economies when it comes to industry adoption in data, analytics, data science and AI. Even though we have some of the best talent when it comes to research in AI and technical skills, our companies don't see as much value in leverage quantitative skills compared to our G8 counterparts. This is evident as many of these top talents ends up in big technology companies elsewhere.
There is a fundamental skills shortage when it comes to people with strong data analysis skills combined with strong business acumen, and most of these people will end up in high paying jobs in a technology company in the US, or a big financial services firm in Canada. Or, they will end up in a top tier consulting firm, but still serving big technology firms or big financial services companies.
It's hard for business executives to invest in something they don't fully understand. Most c-suites have knowledge into what analytics can do, and why it's important. Until they start looking into the data and let their quantitative team be creative and deal with revenue generating activities instead of data manipulation and report creating, the benefits and value is can be vague and elusive. Data and analytics does require a significant investment and there is a lot of resistance to invest in something that there is no plans or foundations for.
Here are the fundamental issues I see across industries and some obvious ways to improve/alleviate them.
1) Organizations don’t have a centralized or core strategy for data and analysis
Many organizations are beginning to recognize the importance of data and analytics and want to do something about it to increase their competitiveness in the market, bring value to their customers and decrease costs. However, most organizations don’t have a core data or data analysis strategy that is in line with their business strategy. Ask yourself this, does your organization have a clear plan on what to do with digitization, data, analytics in the next 5 years?
Ways to improve: Build a data strategy that contains what data you have, what data you want to collect, how are you using them and who is using them to start. Start with the stakeholders and data, then build your way up to analysis and reporting.
2) Disorganized and poor quality data
Before you can do any analysis or build any reports, you need the data. Often, employees from an organization have a difficult time getting data because it can be decentralized (different information silos on different people's computer), owned by a third party, or simply don't know where to look for them. As well, the maturity of the data is often a big issue as well. Just because your IT record the data, doesn't mean it's useful data for your specific purpose(s). It might be in the wrong frequency, it may contain errors, there may be gaps in the data. More often than not, I have to deal with data that is messy, incomplete and attempt to extrapolate information from that, which is difficult, but doable. But I dream of having clean datasets to do my analysis on. Maybe one day.
Ways to improve: A data governance document that outlines who owns what data can help with this. Every company that has data should have a data governance document (and a data privacy and security document while we are on this matter). Perform a data audit to check the quality of the data and see whether it matches your business use cases is really important as well.
3) Lack of capability in data analysis
Many organizations have data sitting in their databases that are not being used. Most of the time the executives will tell me that they know they can find value in the data and they will have certain hypothesis associated to the data, but often they don't have the capability or understanding to determine the cost-benefit of actually doing a project. It is hard to put a value on information, that's for sure.
Ways to improve: I would argue that you can hire an internal or external analysts for relatively low cost to perform an sweeping assessment on where are your low hanging fruits of data usage that is low cost and high impact. That is a good starting point to see your options. As well, giving education for everybody in the areas of data, analysis and technology would bring tremendous value. Finally, bringing in an external expert to facilitate working sessions between high level executives can yield significant benefits and provide clear directions on what needs to be done.
Analytics is not a team, it’s not a person, it’s just a problem solving mindset where people use facts and trends to make decisions. To be a successful company, you want every team and every employee to understand the importance of this, especially in this (crazy) digital economy.