Big data initiatives already exist in many companies however not all are successful. Why?
Enormous data volumes are one thing – getting profit out of them is another. Obviously, this is still the challenge when it comes to handling big data: how do companies discover and use the specific "gems" in their data? A PwC (PricewaterhouseCoopers) study, for example, shows that big data is part of value creation and the basis for business processes in only 19 percent of logistics companies. What hinders companies from making their data volumes correspondingly profitable? Where are the causes? What do the experts specifically advise? Read more in the following eight specialist tips.
1. Setting up a big data strategy
According to the PwC study, many companies said that they still did not have a big data strategy, that their employees were not yet trained accordingly and that the processes within the company had not been adjusted yet. PwC experts are certain that this a mistake, as the corporate strategy is always the starting point for the questions that big data projects are supposed to find answers to.
2. Creating a central organizational unit
The management consultancy experts have also identified a possible hurdle in the fact that the issue usually remains stuck in the hands of the CEO or CIO, while a central organizational unit is often missing in logistics companies. Negligent, they say, as such a unit is decisive in furthering data analysis and can anchor it in the specialist departments.
3. Enabling new thinking
For Axel Oppermann from analyst firm Avispador, success requires the rethinking and optimizing of processes and workflows on the basis of data analysis technologies and know-how – whether they are in-house or applied by third parties. Weather data, for example: a bakery can adapt its production on the basis of such data. A courier company is able to plan its fleet use accordingly. In practice, however, many of those involved still have difficulties with the practical implementation.
4. Breaking old habits
Big data is a part of the digital transformation, and transformation, says Oppermann, means breaking old habits. "But they do die hard. People have a tendency to preserve established routines and patterns. This may simplify life, but it also inhibits adjustment to new realities," says the analyst.
5. Developing application scenarios
In a random sample in the Rhein-Main region in Germany, the business data processing specialists at SRH Hochschule Heidelberg have produced a more detailed picture of big data reality in medium-sized companies. They conclude: "Many companies in the region are lacking creativity to develop specific application scenarios." This means, say the scientists, many companies obviously still have absolutely no idea of the untapped potential.
6. Putting projects in the right hands
Software provider Informatica says in a global study, that only 27 percent of big data initiatives are profitable; 45 percent only cover their costs; and 12 percent of the study participants said they even lose money with big data initiatives. The study illustrates the following correlation: a big data project is more than twice as likely to be profitable if a Chief Operating Officer (COO) or a Chief Data Officer (CDO) takes control instead of the CIO.
7. Guaranteeing data protection and data security
Insufficient value creation also emerges because half of all companies are still highly uncertain when it comes to security, says a study by corporate consultancy firm BearingPoint. "With 50 percent, data protection and data security are front and foremost with the challenges still to be overcome. This is a general cross-sector problem, as companies have to observe both legal and internal regulations and contractual provisions."
8. Integrating big data systems better
While many companies are already using the right tools, there is obviously still a deficiency in coupling these big data tools with the legacy IT. This was the finding of software provider Progress. It says that the processes used are mainly manual, that extensive data exchange is often lacking, and that a 360 degree view – on the customers, for example – is far from the norm in many companies.