The Chief Data officer Summit was held in San Francisco on May 22nd & 23rd 2014. EURA NOVA‘s attendance purpose was to assess companies’ current Big Data challenges and to define an efficient positioning strategy in this highly competitive market. The following article depicts companies’ current expectations regarding Big Data, the approach they adopted and the challenges they face.

A shifting mindset

Nowadays, companies’ mindsets about data are shifting from an “efficiency & cost reduction” perspective to a “higher business value creation” perspective. This change is fostered by companies awakened by the true nature of data. Indeed, data is now considered as a billion $ asset that has been gathered for years at the expense of significant effort. Applying smart analytics on those will allow them to reap the financial rewards of this hard work. Concretely, companies expect the following benefits from this shift in mindset:

1. A reduction of the regulatory compliance burden: the cost to comply to existing regulatory policies in a sustainable way is often high. A data-driven approach can significantly reduce those costs by providing high-end analysis & forecasting capabilities.
2. A customer experience improvement: it is a fact, “Bricks & Mortar” selling points are on the decline. Customers desire to benefit from the same level of flexibility while shopping online. In that perspective, Big Data has a significant role to play as to favor up-sales & cross-sales, customer retention, & services customization.
3. A gain in operational efficiency: the costs of data management & quality can be significantly reduced if properly managed & structured.
4. An improved decision making process: being able to access the right data/insight at the right time is crucial in decision making activities.

At the end of the day, every company expects that this shift in mindsets will enable them to climb up the value pyramid of data & analytics:

Challenges ahead

As companies are currently at the premises of their Big Data strategy implementation process, several challenges are met:

1. Inducing a new way of thinking: As mentioned earlier, a global change of mindset is required within the organization in order to embrace the potential of Big Data. In most cases, this will be fostered through the achievement of small successes build upon existing projects proving the potential value that could come out of a global Big Data approach with tangible & measurable results.
2. Data integration project fragmentation: As a consequence of challenge 1, data integration projects are often fragmented inside organizations. Therefore managing to conserve a global coherence when exploring their Big Data potential is still a challenge for most large traditional companies. In addition to that, those latest have to fight against generation Z companies which flexibility provide them with a strong competitive advantage.
3. A higher degree of collaboration: Integrating data is harder than people generally think. In most cases, 80% of the work in Big Data projects relates to data integration & data quality. In order to solve this challenge, business & IT collaboration is a must that will enable companies to implement the skills & tools necessary to focus data scientist efforts on value added activities of Big Data.
4. Define the role of the Chief Data Officer (CDO): the practical implementation & adoption of an information centric structure will often be made possible thanks to a top-down approach that ensures a coherence between the implemented processes and companies’ guiding principles. This is the reason why a successful Big Data strategy is one that tends to elect a chief data officer. While the exact role of the CDO may be different across organizations, it is important to define the exact scope of the responsibilities that the CDO must take. This role should be defined within the ecosystem of the company in order to deliver the primary goal of the CDO: deliver value from the data assets of the company.

People DO matter

It should be clear now: the technology itself is not enough. Companies need to invest in the people who will process the data to get new insights. IT actors and business must tightly cooperate, the former delivering the potential of new data management technologies while the latter outlines the business opportunities.

The unified vision of the data will lead to new insights that enable the creation of business value based on data products. Any data product starts from the definition of business metrics, further supported by the identification of causing and correlating factors. Based on the previous points, a data product that will improve the business is then proposed.

In the end, the most cutting edge data product is worthless if not used at an operational level. These require user-friendly tools to be built in order to allow any people at the business level to gather new insights. Furthermore, the idea is to capitalize on the people present in the company by training them into the right mindset. This approach should solve the problem of retaining exclusive profiles for working with the data. As a rule of thumb, one can consider that the operationalization of data is reached once insights become available across all execution systems.