What Google’s and Salesforce’s respective acquisition of Looker and Tableau Software means for CIO’s
By: Piyanka Jain
Tags: Analytics, Salesforce, Google, Data Science, Business Analytics

The BI analytics tool space is consolidating to compete against Microsoft’s ensemble of Business Analytics (BA) products, which promises to solve for the entire workflow — data generation, data capture, data storage, data access stratified by persona and data visualization. Why? Because Data is the new currency. The organizations who are able to milk the data would remain to be the leaders in their respective industries. The faster the access to relevant data, the more quickly the business decision makers can react in order to drive user engagement, reduce user churn, and build unmatched product features that continue to delight.

With Google’s announcement to buy Looker for $2.6b last week and Salesforce’s $15.3b deal to buy Tableau Software announced today, a new ‘one-stop business analytics suite of tools’ trend is emerging. Both of these respective tech giants are likely to create a one-stop cloud product that solves for the entire data work-flow from data generation to data access and visualization to compete with Microsoft’s Azure and PowerBI workflow. And, it won’t stop there. I totally expect all three companies to continue developing AI and NLP powered — “ask me any question and I will give you a chart/answer” features that will make data consumption easier for citizen analysts.

However, building a culture of data, like that of Capital One or Airbnb, where every employee can “think data” and “act data” requires more than data maturity. Building Data Culture requires all 4 D’s presented below to line up.

1. Data Maturity: Microsoft’s Azure to Power BI workflow as well the Google and SalesForce acquisition is focused on delivering data maturity — easy, fast, scalable, user-level access to a single source of truth.

2. Data Literacy: Tableau, PowerBI, and all the leading Business Analytics tools suffer from one ailment. That is of low adoption rate — defined as the percentage of seats/licenses that get meaningfully used. It ranges between 20–30% for most of the top business analytics tools, i.e. 70–80% of licenses go unutilized or under-utilized. Can you believe it?

BTW, we are talking here about tools that are very easy to use, with intuitive drag-and-drop features as well as “ask me a question” feature. And adoption is low as it is, even though almost all of the tool roll out is followed by the tool training. Do you know why there is low BA tool usage?

In my 7+ years of working with the large mature organization for developing data DNA for them, we repeatedly arrive at low Data Literacy being the key driver of low tool adoption. Data Literacy is the ability to read and use data to draw a meaningful conclusion to power decisions. Many leaders may be tempted to jump the gun here and say — “oh, I can increase literacy, let’s give people some training on charts and graphs.” I have seen this approach fail where companies fail to move their Data Literacy index even after having strategic tie-ups with top universities and then rolling out mandatory classes for all (which ‘all’ hate). I myself have been part of this bandwagon. Early on, we failed miserably in building data culture for our partner organizations, but thankfully for us, we quickly learned to turn those failures into learnings that we are now able to drive success and transformation for our clients. I will share our learnings shortly.

3. Data-driven leadership: Like any other enterprise-wide strategic initiative, developing a culture of data starts with data-driven leaders. Granted, when we start with any organization, not all leaders are at the same level, however, by the time we are done, most leaders believe in the power of using data to power decisions (because they have already seen the massive movement in key metrics powered by data literacy programs) and are willing to lead by example — holding their team accountable, zero-based budgeting, following decision-making processes for decision-making instead of ‘because I know’-based decisions, etc.

4. Decision-making process: Lastly, a successful data culture needs a data-driven decision-making process to plan by numbers, act and execute, measure and course correct, and look back to evaluate and decide the next set of actions.

In conclusion, here are my top tips for CIO’s driving Data Culture for their respective organization.

a. Data Literacy needs a stratified solution — i.e different job roles require a different level of Data Literacy. For example, a customer support agent might just need to be a ‘data enthusiast’, while a claim analyst would need to be a ‘citizen analyst’.

b. Data Literacy goals are different for different organizations and so even though the Data Literacy personas may be similar across organizations, the rollout is almost always custom to the client’s needs and internal company culture.

c. Developing a Data culture is a change management process and so must be treated similar to any other culture change initiative with full planning, by phases, strong communication, evangelism by leaders, and all the rest.

d. Successful Data Culture transformation always drives top financial and customer metrics — In all of our successful Data Culture rollouts, we have focused on building data chops for the top teams, while they solve top strategic projects for the company. So, our Data Literacy programs show successful movements on revenue, growth, retention, profitability within 9–12 months and so should yours.

The coming months and years are going to be an interesting time for these tech giants. I predict that the business analytics platforms who go beyond data maturity into enabling Data Literacy and a culture of data will gain significant market share. CIO’s and CTO’s looking to invest in data infrastructure in 2019/2020 — do pay attention!