InfoGraphic about Intelligence Hubs as accelerator of the Digital organisation. Five steps how you could think big, and act small to unlock value of Data in your organisation. Contact me for the office A0 poster.
1. Intelligence hubs as accelerator of the digital organisation smartphone went on to purchased on a smartphone Forecast of European online retail sales in 2017 by country in billion Euro's Challenges UNLOCKING VALUE FROM DATA MEANS THINKING BIG WHILE ACTING SMALL. Understanding the dynamics. Implement through a stepwise approach: Acting small to make fact-based decisions essential Thinking big: Intelligence hubs as accelerator the digital organization Acting big to make it stick OVER THE LAST DECADE, DATA HAS BEEN INCREASING IN VOLUME AND DIVERSITY and the speed of production continues to grow. 62 Central Europe Middle East and Africa MAKE DECISIONS BASED ON FACTS: The end of assumption-based decision making. THE DIGITISATION OF DAY-TO-DAY LIFE IS DRIVING MARKET CHANGE. Classical business models across industries are under pressure. BUSINESSES HAVE TO INNOVATE AND SECURE THEIR BUSINESS MODEL TO RELEASE THE VALUE OF DATA. Some are more successful than others to overcome the key challenges. Non-mobile devices Mobile devices Interactions through mobile devices will grow. Today there are 7 billion connected devices in the world, in 2020 there will be 32 billion devices. Data transactions through these devices will grow at 78% a year. In 2016, there will be 12 exabyte data transactions a day - in other words 66 billion pictures sent a day! The digital customer is on the rise. The internet has redefined the consumer decision-making process. The average number of information sources used by shoppers has doubled in the last years. Customer loyalty is reducing as switching becomes easier (80% of customers will abandon a mobile site following a bad user experience). Online sales are growing at 11% per year in Europe. The European internet economy is already 3.5% of total GDP - this will double by 2016, and triple by 2020. There has been an increase of 18% in the number of retailers going bankrupt over the last three years as they have not adapted their business model in time. In businesses, only 5% of data is used, while There are several challenges that businesses face in this process of change: Global challenges. Security: of people have no idea who holds their personal data. The number of privacy injunction applications more than doubled in the last year. People have little trust in the way companies handle their personal data and what they use it for. Online crime: 2.7 billion people use the internet today and this is expected to grow to 4 billion by 2017. As a result, online crime is growing. Organisational challenges. Value: I want to do big data, but dont know how to apply any of the outcomes Capability: professional level Governance: Data management and governance processes arent defined and are fragmented throughout the organisation Technology: We have a fragmented landscape of applications and warehouses Sweden $ 6.5 Germany The digital organisation is on the rise. Mass customisation has become the norm, boosting flexible production and changing the traditional organisation. Next to that, the value chain is increasingly digitised to support flexible production, personalised service and products. The big data market has exploded. A consequence of the digitisation and the growing amount of data, is the growth of the big data industry. The big data technology and services market will grow 27% per year to $32 billion, and that the internet of things will generate 30 billion autonomously-connected endpoints in 2017. In a connected world, emerging economies will drive further data creation. of data today is generated by the West, other parts of the world are emerging. So is Asia expected to be the biggest data producer by 2019. The internet of things will generate 30 billion autonomously-connected endpoints in 2017. Total data volume will increase exponentially. The total amount of data will grow from 5 zettabytes - to nearly 45 zettabytes, that is the equivalent of 62 billion iPhones. Talent matters as much as technology routinely run experiments to test the impact of changes to things like marketing strategies and recommendation systems. Amazon is able to monitor the impact of tiny changes, such as a of making the change, says Mr Wiengend of the Social Data Lab. MANAGE THE PENDULUM EFFECT BETWEEN STRICT SECURITY AND MASSIVE OPPORTUNITIES Accompanying the digital revolution are multiple security and privacy concerns. Cyber criminals in search of financial gain (representing 60% of cyber crime) and intellectual property spies (about 25%) give cause for concern. Companies have to be aware of the security, moral and legal choices they make regarding data protection. Digital shoppers are on the rise and represent a massive opportunity Despite the risks accompanying the increase in online data, there are also opportunities: Reputation can be positively influenced by a security strategy when a large bank openly informed their customers about phishing emails, positive sentiment increased. Customer satisfaction increases when companies react quickly to public opinion. 95% of popular brands have a webcare strategy, including service delivery. Successful companies have response times varying between zero and two hours, while the average response time in the Netherlands is 15 hours. Mobile phones Profit increase 27.5% Fraud detection 80% Security Marketing VS. Fraud reduction 30% Business unit operations Identify a visionary to sponsor your first data insights project that can be executed in eight weeks (small) and will delver insights that exceed the investment at least five times (essential). Data insight projects follow a five step proven approach: A. Formulate hypotheses as your point of departure: Engage resources from business, data science and consultancy, and bring them together into a team to brainstorm on hypotheses Ensure the insights are tangible input for a realistic business case B-D. Prepare, analyse, validate your data: Use your existing infrastructure and analytics tooling, where necessary, complemented by low-investment, open-source software, for the first data insight projects Continuously manage and iterate scope and outcomes with business and project teams E. Manage benefits realisation with a disciplined approach: Formulate clear next steps to capitalise your insights Monitor the insights regularly to justify the realised benefits Share success throughout the organisation to trigger demand We believe that a digital organisation requires an enterprise-functional approach to maximise the potential of available data. By thinking big and initiating an enterprise-wide intelligence hub, companies can speed up their journey to becoming digital organisations. Stakeholders Who should we take into account? Your data playing field should give you insights into who your stakeholders are (customers, regulators, shareholders or employees) This rich set of stakeholders, and limited capacity of an intelligence hub, requires a careful prioritisation to deliver high value to all stakeholders Customer and Value - Why do you need an intelligence hub? What is the customer value the intelligence hub creates? What services do the intelligence hub oer to stakeholders? This goes beyond the value of the analysis itself . The type of service dimensions are: the speed/flexibility of the analytics and type of data that is requested by the business. Capability What are the resources and how do we organise them? Building the capability for intelligence hubs goes beyond recruiting data scientists and procuring software tools. A digitally capable organisation has the following ingredients in place: data driven marketeers and managers, processes to deliver insight projects, benefit reporting platforms, data governance, agile infrastructure and a view on roles and responsibilities Financial - How do we finance and keep costs in control? Most organisations use financial triggers as a driver for change. Initiating data insight projects requires a short-term return on investment (ROI) and shoud be used to drive such experiments. But, a long-term investment is required in the near future to accelerate the transformation into a data-driven organisation. Hence, one should define a budget and cost and performance mechanism that fits the requirements to manage the pendulum between short and long-term investment requirements. Hacking Malware Social Physical Misuse Negative Neutral Positive BUILD A DATA CAPABILITY BEYOND RECRUITING DATA SCIENTISTS AND BUYING BIG DATA PLATFORMS. Building a data capability requires an integrated approach over The skills and knowledge of data scientists are precious. The number of job opportunities for data scientists are increasing: the US is expected to create around 400,000 new data science jobs, but is likely to produce only about 140,000 qualified graduates to fill them in 2015. Analytical methods have not evolved as quickly as technology. Most methods used today stem from the 1950s to 1980s. Due to innovation in technology, application of several analytical methods is now possible compared with a couple of years ago. 0,02 0,01 Risk of mis-using oversimplified analytical software. Analytical software tools make data analytics mainstream, but there is a risk of over-simplification. Programming skills are less important than before. This increases the risk of miss-use of the applications New platforms challenge the way corporations look at IT. There are an increasing number of open-source, service subscription and cloud solutions that companies can benefit from. For example: Hadoop, an open-source technology, has become mainstream. Stage gate | Deliverable Stage gate | Deliverable Stage gate | Deliverable It is our e