What do you see as the main benefit of Big Data?

Big data offers you insights in the context of your organisation. Thanks to analytics, organisations can better sense their environment, seize opportunities and transform their organisation. Descriptive, predictive and prescriptive analytics can each provide insights into the business and as such improve and optimise your performance and increase your competitive advantage. Descriptive analytics enable organisations to learn, sense, filter, shape and calibrate opportunities by providing insights as to what has happened in their environment. This will allow your organisation to better sense opportunities than the competition. Predictive analytics can improve your decision-making across your organisation to help you understand which opportunities are best to be seized depending on their future outcome.

Why is Big Data important for organizations and how can it be used to increase business value?

Big data has often been coined the next “management revolution”, the Fourth Industrial Revolution or “’the next frontier for innovation, competition, and productivity”. While only a few years ago, organisations were still struggling to understand the impact of these trends on their business, big data has now emerged as the corporate standard. Big data analytics affects all organisations, big or small, has an impact on every industry around the globe and is a key characteristic of the organisation of tomorrow. Especially in these ambiguous and uncertain times, analytics enables organisations to sense opportunities. Using large amounts of structured and unstructured data and applying it to advanced analytics enables organisations to understand their environment and seize opportunities, which enables them to remain competitive. Data analytics can help to interpret the business environment, enable managers to act and result in sustained superior performance and competitive advantage. Therefore, the introduction of descriptive, predictive and prescriptive analytics means that the traditional way of decision-making, based on experience and expertise, is exchanged for data-driven decision-making. When organisations provide more people with access to knowledge, power is distributed more equally, enabling employee empowerment within an organisation. This power shift is necessary to fully benefit from big data analytics.

“The datafication of your organisations opens a whole new line of possibilities, which you should approach from an out-of-the-box perspective. Only then will you be able to come up with real added value for your organisation.” 

What would your advice be for any company that wants to start with Big Data?

Knowing what big data is, is one; knowing what a big data strategy and how the organization can benefit from it is two; knowing how to implement that big data strategy is even more difficult. At least, that is how a lot of organizations perceive it. And it must be said; in large process-directed organizations it can be difficult. Convincing the board and defining were to start could be a unnerving task. While in fact the steps that need to be taken are clear and straightforward.

First of all, organizations need to understand what big data is to begin with. Otherwise defining a strategy is impossible. Knowing what big data is can help you to get management buy-in within the organization. Quite often, big data is seen as an IT matter as mentioned earlier, after all you need hardware and software to implement a big data strategy. The hardware and software need to be developed by highly skilled technical big data employees who, especially in the beginning, will form a large part of the big data team implementing proof of concepts and/or a big data strategy. There are also many big data blogs and events around the world targeted at the technical IT point of view of big data. Events and blogs about big data engineering, big data architecture, big data analytics are appearing all over the world. This is nothing strange, as the required IT of big data is different from what we have had so far and therefore sharing information about it is important and valuable.

However, we should not forget that the required IT is merely a means to an end to achieve a strategy defined by the organization. This strategy could be “to increase customer satisfaction” or “to increase revenue” or “to improve the operational efficiency” and the route to achieve that strategy could be big data or any other solution for that matter. If the strategy is “to increase customer satisfaction” it would be strange to define it an IT matter or have the IT Director be the sponsor of the strategy. As IT is merely supportive, senior management or the board should be involved and support the decision to move forward with big data. Especially in the beginning the returns on investments made can be unclear and could potentially be negative in the beginning. Management buy-in ensures that the project is not stopped before any real results can be shown. This should be someone within the organization that understands all different departments, is able to have a helicopter view of the project and is high enough within the company to direct and align different departments. IT is simply too operational for it to be in the lead or to sponsor big data projects.

Once senior management or the board approves the decision to move forward and a C-Level executive sponsors the project, it is important to get together a multi-disciplinary team from all different departments within the organization. Data tends to be kept in silos throughout the organization. Focusing only on one part of the company could result in valuable data sources to be left out. The marketing department should be involved because of the customer point of view. Involve product management to understand how data is gathered in the products or services offered. Involve human resources to apprehend the effect of data gathering on employees. Involve compliance and risk to guarantee that the organization sticks to the four ethical guidelines discussed earlier. Ensure the finance department to keep the budget under control and of course involve IT to build the required hardware and software.

Including all departments within the organization has a major advantage when defining the possible big data use cases; brainstorm sessions will become a lot better when people from different disciplines are involved. Each member of the multi-disciplinary big data team is able to offer a different point of view on data and together a large pool of possible use cases can be defined. It is important during this phase to accept all possible use cases that are brought up during the brainstorm sessions (as in normal brainstorm sessions, ‘no’ and ‘that’s not possible’ do not exist). It is essential to let creativity flow, as this will allow you to find new data sources previously not thought of.

Once a few dozen possible use cases have been defined, it is time to develop criteria to rank all use cases. It will help to divide the use cases into different categories first, such as the use cases that fix bottlenecks within the operation or use cases that improve the efficiency of business processes. Use the criteria to rank all use cases in the different categories. Criteria can be the impact on IT, the impact to implement the solution and/or a possible value proposition. It is not necessary to completely develop a scenario analysis for each use case, as there are too many unknowns at this moment.

Based on the criteria and the selected categories it is possible to select the Proof of Concepts that will be realized. The multi-disciplinary big data team should be able to realize the Proof of Concepts with minimal efforts. It is better to fail fast and fail often than to develop a complete solution and notice in the end that something was wrong. While big data has the potential to bring a lot of positive results, it is possible that this is not evident from the start. Don’t be afraid to fail and restart in this phase, as it is part of the learning curve how to deal with big data and to better understand how your organization can best benefit from it. For each organization after all, the benefits will differ.

The moment the first results come in from the proof of concept, it is important to share the results immediately with the entire organization. It will help to get the entire organization involved in the big data efforts, as for organizations to truly succeed with big data, an information-centric culture should be present. If the results of the Proof of Concepts are positive, it is time to expand the multi-disciplinary big data team throughout the organization and to start more and larger projects. With the lessons learned, it will be possible to extrapolate the results of the first projects to the new projects and to better define a possible ROI, IT impact, possible process implications and other important criteria. From there on, the entire process starts all over.

“The key of digital transformation is data, therefore, organisations need to start with collecting data if they wish to succeed in digitally transforming their organisation.” 

What is the importance of Big Data with regards to Digital Transformation?

The key of digital transformation is data, therefore, organisations need to start with collecting data if they wish to succeed in digitally transforming their organisation. In order to achieve that, the first step is to datafy your organisation. Datafication is the process of making a business data-driven, by transforming social action into quantified data. It involves collecting (new) data from various sources and processes using IoT devices or creating detailed customer profiles. Datafying your organisation starts by making your office, your workplace, your processes and your products smart. This will make previously ‘invisible’ processes traceable so that they can be monitored, analysed and optimised. Thanks to the lowering costs of sensors, increasing low-cost bandwidth, cheap availability of cloud-computing and processing capacity as well as numerous connected devices it has become easier and cheaper to make these processes smart. It will enable you capture data consistently and universally across different processes, products and workplaces. Next to previously offline customer touchpoints, of course, you can also collect data at the online customer touchpoints, such as social media interactions. The data can then be used to create insights using analytics or enable actions using smart contracts. Of course, data quality is of utmost importance when datafying your organisation. Low-quality and biased data will be useless and can even cause damage to your organisation and customers. When looking at your processes, look at it from a new angle. The datafication of your organisations opens a whole new line of possibilities, which you should approach from an out-of-the-box perspective. Only then will you be able to come up with real added value for your organisation.

How do you see Big Data developing towards the future?

Big data will only grow in importance. As I describe in my latest book – The Organisation of Tomorrowevery organisation is a data organisation. A data organisation comes with massive opportunities and responsibilities. It enables you to offer the best customer service and deliver great products, but only if you respect your customer and the data at hand. Ethics, privacy and security should be engraved in every employee. With big data becoming more important in society, privacy, ethics and transparency will also grow in importance.

About the Expert – Dr Mark van Rijmenam 

Dr Mark van Rijmenam is Founder of Datafloq. He is a highly sought-after international public speaker, a Big Data and Blockchain strategist and author of the best-selling book Think Bigger – Developing a Successful Big Data Strategy for Your Business. He is the co-author of the book Blockchain: Transforming Your Business and Our World, which details how blockchain can be used for social good. His third book, The Organisation of Tomorrow, discusses how AI, blockchain and analytics turn your business into a data organisation. He is named a global top 10 Big Data influencer and one of the most influential Blockchain people. He holds PhD in management from the University of Technology Sydney on how organizations should deal with Big Data, Blockchain and (Responsible) AI and he is a faculty member of the Blockchain Research Institute in Canada. He is a strategic advisor to several blockchain startups and publisher of the ‘f(x) = ex‘ newsletter read by thousands of executives.