Of all the disruptive technologies, Big Data must be the biggest and the most hyped. The spread of social media and e-commerce and of device penetration has created vast amounts of easily accessible data of huge potential value across many areas of business.
The sums being invested in are also vast. According to a recent IDC report, the big data analytics industry saw revenues of $130 billion in 2016. Unfortunately the capability to analyse and then use data effectively is not keeping pace with the rate at which data is created.
In a recent article, Dr. Manu Carricano, Associate Professor in Operations, Innovation and Data Science, and the Director of the Big Data Analytics Executive Program at ESADE Business School, offers valuable advice on how to move beyond the hype:
We are now beyond the hype point in Big Data and Analytics. With two consequences. First, a greater number of organizations have started initiatives, and some are having successful projects in production. Second, as teams and projects are scaling up, the time for results is coming...and Boards’ patience is vanishing sooner than expected.
So, do we see results yet from the many initiatives started? In the vast majority of cases, the answer is no, and several root causes can be identified:
- Access, quality and data ownership
- Silo organization, even with active Data initiatives
- Culture of ‘pilot’ and lack of support from Top Management
- Lack of business acumen/orientation of the teams
- Dichotomy between ‘data engineers’ and ‘data scientists’
- Algorithm ‘infertility’, lack of trust on recos, etc...
How to overcome these difficulties, and make sure that your organizations goes full steam towards a successful data-driven transformation?
The key to success here is, from the early days of an initiative, to develop a holistic view on the process, blending its two facets (Data Architecture & Management on one hand, and Data Science & Analytics on the other hand) while keeping results delivery on track.
As start-ups are working in an agile way to deliver early their MVPs (most viable products), leaders of Data-Driven Transformations should focus on delivering in fast turnaround analytical products that impact the business.
This is the underlying objective of the ‘Big Data & Analytics Canvas’ that we have developed over the last couple of years with organizations facing complex analytical transformation.
The canvas is structured on 6 main blocks:
- Business: formulation of Objectives; Actions to be taken (Deployment); Measured Impacts.
- Data Integration: Data Sources; Enrichment; Data Architecture decisions.
- Data Exploration: Discovery.
- Data Visualization: at the centre as it allows to quickly deliver results and scale up the number of users around the initiative.
- Insight Generation: building an insight engine is key, and can be deployed around two dimensions: descriptive analytics (for many organizations, less is more) and predictive analytics.
- Decision optimization: many initiatives focus on data science, but forget prescriptive methods rooted in OR. Prescriptive is the higher level of analytical maturity and where business impacts happen.
The approach is simple, but its constant monitoring, in a Data-Driven ‘war room’ ensures a pragmatic overview of this complex transformational process, a clear communication to the key stakeholders (and particularly Top Management), and an efficient tracking on progress and results delivery.
For more information, and if you are interested in leveraging the Big Data & Analytics Canvas, join ESADE’s brand new Executive Education course on Big Data & Analytics.