APRIL 2015 Discover the WHY, WHAT and HOW of Big Data By Geert VROMMAN & Peter RAKERS Size doesn t matter When managers inject data and analytics into their daily operations, they can deliver productivity and profit gains that are 5 to 6 percent higher. In other words, Big Data analytics challenge the gut feeling of the manager on the level of strategic decisionmaking and therefore produces quantifiable results such as better customer knowledge (profiling, loyalty, segmentation, etc.), new products and services, a higher efficacy, more profit, smarter and more automated processes. One of the difficulties when starting with Big Data analytics is the faith one should have in the outcome, without knowing what that will be. The challenge starts by filtering relevant information out of piles of data in search for new insights. The exercise itself shouldn t always have to be big. Big Data is still considered as a hype but many companies are starting with new initiatives in this field of analytics. Nevertheless, the technological evolution and the advanced analytical methods are here and they are here to stay. As with any organizational change, a clear internal communication and realistic mindset is needed in order to explore the opportunities of Big Data. The intention to do more with data should be communicated throughout the company giving all stakeholders a chance to think about applications in which data could make the difference, now and in the future. Big Data projects require a unique combination of database technical skills, statistical skills and business-strategic skills in order to translate algorithms into conclusions that can be anchored into the organization. Data science is in full evolution towards a high tech job description but for now, companies still need a mix of internal competences in combination with external strategic partners, which are more familiar with the latest analytical methods. In this white paper, Cropland would like to elaborate on the why, what and how of Big Data Analytics, anno 2015.
2 About the why, what and how This white paper about Big Data Analytics is anything but a joke but it can t hurt to start with one we borrow from Prof. Kofman (Kofman 2014): Two hikers encounter a bear in the woods. One of the hikers immediately starts to change his boots for running shoes. "What are you doing", says the other hiker, "you cannot outrun a bear!" "It doesn't matter", responds the first one, "I only have to outrun you." Indeed, when companies inject data and analytics deep into their organization, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition. (Barton and Court October 2012) And so, chances are you make it out of the woods, long before others do. This white paper is about getting smarter, leaner, meaner and more profitable thanks to advanced data analytics. The good news is that size doesn t matter at all. WHY speak about Big Data? DATAFICATION In the book Big Data (Mayer-Schönberger and Cukier 2013), we can read the true story of Commander Matthew Fontaine Maury, a young 19th century U.S. Navy officer. After he was injured in 1839 and no longer fit for sea, he became Head of the Depot of Charts and Instruments. What he found was a treasure of historical data on winds, waters and weather on well-defined places and times. He started to compare all available logbooks of Navy captains crossing it with merchant ships trades (old English for 'path' or 'track' only later associated with commerce). He asked all parties to throw bottles in the sea with date and location information for others to pick up. He considered every ship to be a floating observatory during its journey. He created a nautical network and enabled data sharing between Navy officers and merchants for the benefit of all, namely shorter and safer trajectories across the Oceans. In 1855, he published his work 'The Physical Geography of the Sea' in which he presented new navigation tables making transatlantic journeys shorter with 1/3 rd of the time. In this way, Maury became one of the pioneers of something we call datafication: the extraction of data from material that apparently had no (more) value to something or someone.
3 This story of datafication is an example on how to put something in a quantifiable format so it can be tabulated and analysed. It is different to the term digitalization being a process of converting analogue information into zeros and ones of binary code for computers to handle. Later, Kenneth Cukier, the co-writer of Big Data (Mayer- Schönberger and Cukier 2013) would add one of our favourite quotes: More data don t let us see more. More data allows us to see new, better, different. NEW TECHNOLOGY Moore s law still serves as a lodestar regarding the speed of technological evolutions. In 1965, Gordon Moore predicted for the computational power to double every 2 years. Thanks to this evolution, computers became faster and working memories became bigger. In parallel, the algorithms that are used in Big Data evolved accordingly. On top, the more recent evolution, referred to as the Internet of Things (IoT), where commodity devices such as smartphones, tablets, wearables, household machines, manufacturing machines, sensors, etc., are connected to the Internet, show an exponential growth in data becoming available. The IoT brings new applications to the consumer but companies also embrace the opportunity to connect their products and manufacturing processes with the Internet. Faster computers, a diverse set of (un)structured data and enhanced algorithms deliver a powerful cocktail that we conveniently summarize as Big Data. The challenge consists in filtering relevant information out of piles of data in search for new insights. So, it challenges the gut feeling of the manager on the level of strategic decision-making. TROP IS TOO MUCH AND TOO MUCH IS TROP Due to digitalization, every department in the organization has its own ICT support system(s). When new Internet technologies are added to those software packages and when the IoT is integrated in corporate solutions, an urgent problem emerges: an explosion of data. Where, in the past, managers had to make decisions in situations where no or little information was available, they step into an era where the abundance of data generates more confusion than clarity. In the end, companies get stuck in the typical data silos they cherished so much. Even more, what might be a pure transactional data derivate for one can be strategic information for another without understanding the complementary value. The first step towards Big Data is breaking down these data silos in order to unleash the full potential of crossfunctional data.
4 DISRUPTION Big Data and advanced analytics are good news but not necessarily for everyone. Jaron Lanier describes the most critical view: Siren Servers are an elite network of companies characterized by narcissism, hyper-amplified risk aversion, and extreme information asymmetry. They gather data from the Internet, often without having to pay for it and analyse these data using the most powerful available computers, run by the very best available technical people. The results of the analysis are kept secret, but are used to manipulate the rest of the world to advantage (Lanier 2013). Recently, the European Commission challenged Google on their search engine practices as being too selective. Indeed, for Belgian companies, the first confrontation with Big Data is mostly disruptive. Due to datafication and the worldwide availability of open source information, a new player who does not follow the well-established rules of the game can disrupt a complete business sector. It is a mistake to think that it is only applied by Siren Servers like Google, Facebook and Amazon. Uber taxi was the first disruptor with nationwide media coverage in Belgium. But meanwhile, we got familiar with Netflix and bhaalu (new competitors for legacy TV networks), 3D printing for classic manufacturers and all kinds of wearable devices (Quantified Self) having a huge impact on the (first line) healthcare system. Clearly, it is happening in many business areas although hidden under the waterline. But the common theme of disruption is getting smarter with data. Peter Hinssen asked the million-dollar question in his blog: Are the risk-averse and number-crunching old boy networks of our boards of directors the best protection in this Age of Disruption or do we need to rethink the system before it s too late? (Hinssen 2014) Recently, we combined the findings of our own survey with the results of a survey conducted by Data News in order to determine two simple but important axes for Big Data positioning: a. How fast is the Big Data adoption in your organisation? b. Where, in your company, do you see the impact for Big Data? We illustrated these 4 quadrants in order to describe the possible positions of companies where Big Data become significant in the respective business sectors. For more information about this scenario exercise, please contact Peter Rakers at info@cropland.be. In the end, the question about why you should start with advanced data analytics is simple: what will happen if you don t?
5 HOW to start with Big Data? THINK BIG, START SMALL It sounds like a logical first step but it s difficult without data. So it is better to start with the collection of all available data and, if necessary, the exploration of some new data sources. An important question rises regarding the kind of data that should be gathered. In order to answer that question, the company strategy and the connection with Big Data needs to be clear. Not fully from the very beginning but some pathways should be visible in order to make some clear choices in the near future. Future scenarios should help to streamline the Big Data opportunities. Scenarios are possible futures of the company based on the critical success (or failure) factors of tomorrow. It is a strategic journey in search for the added value of advanced analytics. The result of this exercise is a clear list with opportunities linked to the strategic priorities of the company. THE PROOF OF THE PUDDING IS IN THE EATING A common problem is that companies are able to identify all relevant data sources but then afterwards wait too long to start a first proof of concept. In most cases, there are lessons to be learned about the data collection and the whole process risks to be repeated if first results only become available after 6 months. The world turns fast in a Big Data world. Thanks to the technology of today, it is more convenient to start several experiments in parallel applying the lessons learned directly into the process. So don t wait too long before starting with the actual analysis. Pilot projects serve exactly to isolate relevant data and search for the cause-effect in data labs in order to profit immediately from the new insights Proof of concepts support the possible next step, which is the development of a true data strategy. A crucial part is the translation of algorithms and conclusions into the daily practice of the operational managers. In the end, they will have to use Big Data in order to adopt data as raw material and a new source of success. It is, anno 2015, still a hype and many persist in giving it no more credit than just a fashion trend. Nevertheless, as with any organizational change, a clear internal communication and realistic mind-set is needed in order to explore the opportunities. The intention to do more with data should be communicated throughout the company giving all stakeholders a chance to think on how data could make the difference. DATA CORRELATION Companies got familiar with Business Intelligence (BI) in the 90 s where the objective was to build data warehouses and to use reporting tools. The original goal of data
6 warehousing was to store and integrate all heterogenic data in a central place in order to keep history, to detect trends and to enable management analysis. In the end, especially the integration part was a big burden for most companies. Many BI systems turned out to be department-specific, only fit to deal with a particular domain within the company and so, they quickly became data silos. Meanwhile, lots of definitions are linked to the term Big Data. One of the V-definitions stands for variety of data indicating the challenge to bring all sorts of data and data types together. It s crucial for a Big Data proof of concept to question the existing data silos within the company in order to make sure all relevant sources are used to full extent. It must be avoided to exclude features or parameters from the beginning in order to make all correlation opportunities as broad and objective as possible. 3 DATA LEVELS Besides the obvious correlations with internal data, it can be useful to explore the potential of external data. It is advised to research external sources, not yet correlated today and/or not yet seen as valuable to the company. They can offer new information and insights due to the uniqueness of this opportunity. In the near future, data ecosystems, named data pools, will emerge through the collaboration of companies, which are willing to share data in order to individually get better insights thanks to data enrichment of the aggregate. Cropland investigates the feasibility of different forms of data pooling. You will find more information on our website www.cropland.be. THE DATA TEAM Big Data projects require a unique combination of database technical skills for data extraction and preparation out of the different company systems; statistical skills for the development of predictive models and pattern recognition; and businessstrategic skills in order to translate algorithms into conclusions that can be anchored into the organization. Data science is a new field of interest and data scientist is in full evolution towards a high tech job description. For now, companies need a mix of internal competences in combination with external strategic partners, which are more familiar with the latest analytical methods.
7 WHAT is to be expected from Big Data? LIFE IS LIKE A BOX OF CHOCOLATES The question everyone would like to be answered right away is: What will be the return? One of the difficulties when starting with Big Data is the faith one should have in the outcome, without knowing what that will actually be. Often, advanced analytics result in new insights and unexpected findings. It doesn t mean that everything should be accepted blindly from the beginning but having an open approach and a healthy entrepreneurship is surely an asset. 4 APPLICATION AREAS As Big Data needs to support the strategy of the company, most projects will result in quantifiable results such as better customer knowledge (profiling, loyalty, segmentation, etc.), new products and services, a higher efficacy, more profit; and/or smarter and more automated processes. At Cropland, we structured data science into 4 application areas in order to give it more meaning: BEHAVIBILITY TRACEABILITY CONNECTABILITY READABILITY In BEHAVIBILITY, we mainly research the behaviour of people and organizations. Loyalty, purchasing behaviour or intentions, cross-selling, profiling, etc. are some of the Big Data subjects. When data are gathered based on time windows, locations or distances, we talk about TRACEABILITY; this application could be used to determine future shop locations, to get insights on the logistic fleet movements, etc. As the Internet of Things is connecting all kinds of devices making new data becoming available, we apply Big Data in the area of CONNECTABILITY, which focuses mainly on detection, prevention or predictive modelling. Finally, we extract added value out of digital text in the area of READABILITY, the data science that is used to optimize workflows, to enhance data quality and to automate document processes.
8 About Cropland Geert VROMMAN and Peter RAKERS founded Cropland in 2013, supported by the genuine belief that organizations should anchor DATA DRIVEN DECISIONS in their daily operations in order to make it a sustainable foundation for the future. In this vision, we cultivate data and enable managers to optimize the effectiveness of their organization. Cropland is a unique team of strategic consultants combined with data scientists; together, we go out in search of fertile soil for your company, your cropland. References Barton, Dominic, and David Court. October 2012. Making Advanced Analytics Work For You. Harvard Business Review 90 (10). Hinssen, Peter. 2014. Should You Disrupt Your Board? LinkedIn Blog. Kofman, Fred. 2014. Doing your job may be hazardous to your career. LinkedIn Speaker Series on YouTube:. Lanier, Jaron. 2013. Who Owns The Future. New York: Simon & Schuster. Mayer-Schönberger, Viktor, and Kenneth Cukier. 2013. Big Data. New York: Houghton Mifflin Harcourt Publishing Company. Copyright 2015 CROPLAND. All rights reserved.