Big Data and Business Analytics is forecasting revenue of just over $150 billion in 2018, a figure that demonstrates the industry's immense importance, and it is clear that organizations must now understand how important working with data is to business success.
But even though there has never been so much data available than nowadays, most companies still fail to process the information to make it profitable. They don’t realize how to operationalize data insights and turn them into a practical strategy. In order to ensure that managers do not face this flood of data again in 2018, this article provides five key steps that help to unlock the true potential of the data.
1. UNDERSTANDING THAT MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE CAN CONTRIBUTE ENORMOUSLY TO SIMPLIFYING COMPANY PROCESSES
Only a few years back Machine Learning (ML) and Artificial Intelligence (AI) were technologies to be implemented at some point in the future. Meanwhile, they have become an integral part in many companies and modify structures and the way these companies operate.
Future forecasts suggest that this development will continue to grow: In 2020, around 85 percent of all consumer interaction will be automated, without the help of humans. People will need to learn how to work with future technologies such as Machine Learning and Artificial Intelligence to process data and draw conclusions from algorithms. Take customer interaction for example: ML and AI send customers a simple answer before more complex requests are passed on to a real person. So, that person will not have to process each and every request manually.
These kinds of technologies allow us to process data quickly and in real-time to standardize and simplify workflows and business processes. Ultimately, this also means that critical thinking will become even more important.
2. REFLECTING ON A COMPANY'S CORE BUSINESS PROCESSES
To be successful with Big Data, a company needs to understand what core business processes look like and how they work. Take a sales process in distribution, for instance. Is the entire process clear, from lead identification to delivery?
Once there is a clear map of every single process component, we can identify the potential power of Machine Learning or Artificial Intelligence.
3. CONSOLIDATING AND CREATING A CENTRAL DATASTORE
A major problem is that too much effort is still required to compile the data for each individual use case. Often, the collected data is separated by departments, which leads to data silos. Ultimately, this not only means a lot of time-consuming work, which is carried out twice over. It also lacks insight into other projects and processes within the company. This in turn leads to greater challenges in everyday business and does not reflect a precise picture of customer behavior.
Providing unified and user-friendly data can accelerate the creation of insights and process automation. It is true that finance, human resources and sales departments have different views of the data and therefore use them differently. However, detailed insights into the way in which different business units use Big Data help to create synergies.
4. CREATING THE RIGHT TOOL SETS
Companies need to set up a kind of data box with matching tools so everyone can use the data to work in a standardized environment. Open source software is one option. Ultimately, however, companies need to create a suitable system to produce data models and to scale larger sets of data.
5. DEFINING ROLES AND RESPONSIBILITIES
By 2020, more than 40 percent of data science tasks will be automated. As a result of this automation, the distribution of roles and tasks among employees in the company becomes much more specialized. It is therefore important to clearly define in advance which area of data analysis each person is responsible for within the organization. As priorities are constantly shifting, communication roles and responsibilities leave no room for errors.
CONCLUSION: 5 STEPS TO SUCCESS
Successfully linking these components will invoke a creative flow for change to make sure the company is ready to enter the data-driven age where man and machine work together. Managers need to understand the central mechanisms of their company, clean up their data, build data playgrounds and ensure that every employee knows the rules and how to implement them.