In recent years most companies have been involved to a greater or lesser extent in digital transformation projects. Although there is a lot of literature about what digital business transformation is, a rather useful definition can be, according to Gartner: “the process of exploiting digital technologies and the capabilities that support them to create new digital business models”.
This change in business strategies based on taking advantage of different technologies and digital tools such as cloud technologies, the internet of things, mobile applications, social media, etc.
One of the technologies that present the most possibilities for the company is big data. However, many managers and directors in companies still do not understand what it is and how they can use it in their digital transformation strategy.
What is big data?
Companies generate and store a large amount of data in all their departments: personal and purchasing data of customers, location and status of assets, R&D, etc. The analysis of this data, along with other data that may come from different internal and external sources, allows companies to find hidden relationships, at first sight, trends or patterns that can help reduce costs or grow the business.
In short, big data is a combination of all the tools and processes used to manage large data sets and is based on the principle that the more knowledge you have about any situation, the better you can analyse and predict what will happen in the future.
Big data in digital business transformation
As soon as a company begins to operate, however small it may be, it will generate information: the customers who buy and at what times they do so, the products they buy, the income, margins and profits, production incidents in equipment and facilities, the status of legalisations, etc.
Any company generates information and data, but the importance of these is not given by the number of them, but by how they are used. There are very few cases in which a company cannot take advantage of the information it generates.
Big data is the most popular tool in all companies that have begun the digital transformation and it is so because it can be applied to practically any sector: the education sector, the health sector, the transport sector, banking, etc. are just some of the sectors that have changed their way of doing things thanks to the use of big data.
There are examples of companies that take obvious advantage of big data, such as Google or Amazon, but any other company can benefit.
An example of a company where the use of big data for digital transformation is less obvious could be the Shell oil company.
A few years ago, they found new oil deposits using seismic wave data and it was an inefficient system, with which they did a lot of tests that often came out negative. Over time, it began to store information on all characteristics of the land where the tests were positive thanks to the use of sensors in all its instruments. The amount of data they began to handle increased 50-fold and they hired a team of 70 people just to analyse it. From the analysis of the data from their tools and the use of this data to search for similar terrain the probability of hitting an oil well increased enormously.
How to start working with big data in your company
To use big data in a company you need, of course, data analysis capabilities, both hardware and software. However, there is no point in collecting and saving all your data without a human team that can interpret it.
Each member of this team needs to know how to handle analysis tools and also how to interpret the data acquired. Besides, they must know the company they work for to know what solutions and changes in strategy can be most effective in the short and long term.
A big data project requires a process and a time to mature. Data collection alone cannot be done overnight and several decisions have to be made before starting:
- What data to analyse.
- How it will be combined.
- What will and will not be relevant.
- What data from competitors and other external sources are interesting and accessible to us.
- Once we have the data, what actions can be taken with the information obtained to make it useful.
In short, to obtain maximum benefit from the implementation of big data, we can focus on the three Ts model proposed by BCG:
- Team. Incorporate or search within your organisation for a team of experts in data analysis and extensive knowledge of the business.
- Tools. Invest in the acquisition and implementation of appropriate tools that allow your team to develop its full potential.
- Tests. Execute different experiments in not very long periods of time (2-3 months), observe the results and adapt your infrastructure until the best solution is found.
Problems in the implementation of big data
The use of big data is not without its problems and issues that need to be taken into consideration, both in the implementation and in the execution of the projects.
Implementation barriers
The first problems we may encounter when implementing big data projects in the company may be
- Lack of suitable personnel: given the novelty of the sector it can be difficult to find personnel with sufficient knowledge both to handle the tools and to properly interpret the subsequent data.
- Lack of labour flexibility: both managers and workers may reject big data as something unknown to them and prevent its implementation, either out of mistrust or even laziness.
- Lack of organizational flexibility: like the above, there may be no reluctance to analyze the big data, but also no willingness to use the information obtained to incorporate it into the decision-making process.
- Difficulty in finding what data to analyse: when starting the process of incorporating the big data into a company it can be difficult to find where to find relevant data and how to extract it for analysis.
Problems in implementation
Once the initial difficulties have been overcome, it is very important to consider other aspects that may lead to legal problems or reliability of the tool, such as
- Confidentiality of client data: studying clients until their behaviour can be predicted, knowing data about them based on data that they themselves are not aware of, etc. opens up an ethical debate that is far from being resolved. It could be summarised in the question: to what extent is a company entitled to study the customer?
- Data privacy: when storing this amount of data it is not only the storage capacity or access to the data that matters, it is also increasingly important to protect the data from any unauthorized intrusion, whether from unauthorized access by employees of the same company or external access.
- Reliability of the data and its analysis: both in collecting the data and in analysing it, mistakes can be made which render the use of the big data useless or, worse still, provide us with false data which lead us to make decisions based on unrealistic circumstances
