7 prerequisites for sustainable analytics success
Crossing the chasm between ‘islands of analytics’ and a sustainable enterprise analytics capability.
By Gahl Berkooz
IDC forecasts that spending on data and analytics technology and services will grow annually at a rate of 11.7 percent over the coming five years[i]. Why are companies prioritizing their investments in data and analytics over other technologies? Because new data about customers, as well as, data streamed from connected products is promising insights that will transform customer relationships, products and business models. However, a recent Gartner survey cites a tapering of investments[ii]. I believe this is because companies are realizing that pursuing analytics proof-of-concepts and exciting isolated stories is neither sustainable nor scalable.
Here are seven prerequisites required to turn investments in analytics into sustainable and scalable business outcomes:
1. Analytics are prescriptive and deployed to make operational decisions with a closed measurement loop. A large durable goods manufacturer wanted to quantify the value the IT portfolio to its product development function. It developed an advanced analytics model that forecast the contribution of an IT project to product development efficiency. The model rank ordered the projects, and prescribed an optimal portfolio[iii]. It also enabled verification of a project’s benefits post deployment by comparing them to the benefits claimed at the time the project was approved. Traditionally, the senior directors and VPs decided the portfolio. This was now replaced by the recommendation from the model (with minor tweaks from leadership). For analytics to deliver on their potential the company needs to embrace analytical methods for decision making where feasible, with a closed loop to monitor performance.
2. The company creates and continuously improves Analytical Data Assets such as Customer 360, Product 360 etc. A bank wanted to increase its mortgage sales by improving the conversion rate of customers looking for mortgage information on its web site. Because mortgage was traditionally handled as a silo product, that area did not have information to tailor personalized offers for the customer. To address this, the bank created a data set that combines information about the customer across all products, a Customer 360 Analytical Data Asset. The new data asset enabled powerful analytical models that generated personalized offers with superior take-rates. Analytically mature companies develop integrated data assets in critical areas such as “customer,” “supplier,” “plant,” etc.
3. Data is a corporate asset supported by all stakeholders. Data circulates throughout the enterprise, it is created in all areas and flows into others. Analytics is a grand collector of data. If the data is polluted, hard to get to, or does not represent the physicals of the company it impedes and distorts insights. Analytically mature companies have created processes, governance, and a culture to create timely, accessible, accurate, and high quality data. They continuously work on improving the value of their data and its availability, and route out… Read more
Latest posts by Eric Axelrod (see all)
- Metadata Automation |Tableau Community - March 1, 2017
- How Amazon Will Ride Big Data To $1 Trillion Market Cap - January 22, 2017
- Why physicists are a good fit for data science jobs - January 16, 2017