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Companies from all industries are realising that their customers leave a lot of useful data from both online and in-store interactions. The use of predictive analysis is one way in which companies can make the most of available data to optimise business processes and achieve higher efficiency levels. How have companies achieved this?

The essence of predictive analytics is insight as opposed to hindsight. It is not so much about the data, but about the meaning and signals that can be inferred from the depths of such data. The evolution of widely available and accessible analytics platforms has provided access to sophisticated statistical models for companies of all sizes to improve their everyday business.

For example, Walmart, a renowned retailer, relies on analytics to link the online and offline worlds to compete with Amazon. The company takes data instantaneously from its point-of-sale systems and incorporates it within its forecasts to assess which products are likely to sell out and which have underperformed. Combined with customer online behaviour patterns, this provides a huge amount of data to help Walmart prepare for a rise or fall in product demand. These forecasts also allow Walmart to personalise its online presence, showcasing products to specific customers based on the predicted likelihood of making a purchase.

The next example relates to the travel industry. It is notoriously competitive, with volatile peaks and troughs in demand and many low-margin routes. This can leave travellers in the dark, unsure of the best time to book. This makes it a field ripe for the power of predictive analytics, a fact that has seen the travel information provider, Hopper, grow dramatically. It stays one step ahead by predicting future pricing patterns and alerting travellers of the cheapest times to buy flights to their preferred destinations. It does this by watching billions of prices every day and, based on historical data for each route, anticipating how the trend will develop.

An interesting example is also that of Under Armour, a company that produces physical fitness products, but also apps and wearable devices to tie the offline and digital worlds together. Analytics is used by Under Armour to perform tasks such as sentiment analysis and social listening to understand what customers think of the brand, and where the gaps in the market are. This has led the company to focus on becoming a digital fitness brand, an initiative that has seen it carve a new niche in a saturated market. Knowing where to spend a firm's advertising budget is essential, but so is knowing where not to spend it. Predictive analytics allows companies like Under Armour to hone in on the areas that will deliver the greatest returns and reinvest budget that would otherwise have been spent inefficiently.

How does European privacy law, General Data Protection Regulation (GDPR) that restricts how personal data is collected and handled, affect consumer privacy from the Internet of Things (IoT)?

Consumers have long questioned just what internet giants, such as Google and Facebook, know about them and who else can access their data. But these companies have little incentive to give straight answers. GDPR focuses on ensuring that consumers or more broadly, data subjects, know, understand and consent to the data collected on them. With the IoT, personal data is collected in a continuous manner at times, posing a risk to the security of the respective data and therefore to the privacy of the data subject.

The first problem arises due to the difficulties in ensuring IoT security. Both industry and academic researchers are still searching for the most efficient methods in this area. Since GDPR places great emphasis on security and privacy, with significant fines in the case of a data breach, especially if the breach takes place as a result of poor security, we can see the first conflict between regulation and the IoT. At the same time, GDPR does not require specific methods to be used for security. Each organisation is given the possibility to choose their method in accordance to the systems they use, their financial possibilities and the risk level. 

Another issue is making sure the consent to process data is obtained in compliance with GDPR. Asking the data subject for consent before they start using each device could be an option - but can we really consider all the situations where data will be collected? The issue is still under debate, with no clear answers provided.

Another sensitive issue under GDPR is processing the personal data of children. Those under the age of 13 should not be able to express consent on their own for processing in relation to online services. Many IoT devices are used by children.

Another challenge that rises with the IoT is knowing where the data is at all times. This means location, as well as who has the right to access it, how the data is used and to whom it is disclosed. According to GDPR, a data subject has the right to be informed of this and more, at any given time. With the IoT, with the use of so many devices by each data subject, the risk of losing track of the data is not negligible.

The key is to look at GDPR as an opportunity and not as an impediment. An opportunity to improve security and privacy. To offer tangible rights to the data subjects, and to offer better services overall.

The purpose of Business Intelligence is to support better business decision making. What are the Business Intelligence (BI) trends, since the systems are data-driven Decision Support Systems (DSS)?

Data quality/master data management, data discovery/visualisation and self-service BI are the three topics practitioners have identified as the most important trends in their work. The importance of data quality and master data management is very clear: decision-makers can only make the right data-driven decisions if the data they use is correct. Without adequate data quality, data is practically useless and sometimes even dangerous.

Data discovery is a business, user-oriented process for detecting patterns and outliers by visually navigating data or applying guided advanced analytics. It requires skills in understanding data relationships and data modelling, as well as in using data analysis and guided advanced analytics functions to reveal insights. Interactive and new visualisation types enable decision-makers to see, within an instant, major trends, as well as spot outliers. Visualisations make use of brain pattern recognition capabilities to digest information at a glance or even pre-attentively. Visual analysis is an important feature that is increasingly being sought by enterprises seeking more efficient ways for decision-makers to absorb and act on data.

Self-service BI tasks are those that business users carry out themselves instead of passing them on to IT for fulfilment. The aim is to give the users of BI tools more freedom and responsibility at the same time. At its heart lies the notion of user independence and self-sufficiency when it comes to the use of corporate information, which leads to the decentralisation of BI in an organisation.

What is the correlation between blockchain and Business Intelligence?

Blockchain can offer several implications for the BI milieu. Within the context of data and BI storage, blockchain can be used to drastically improve security and decrease dependency on central entities. By introducing blockchain, companies can benefit from reliable databases on decentralised, encrypted and non-editable ledgers, embracing the full potential of the technology in order to streamline and secure their data storage. In banking and other industries, the main drive for adoption of blockchain technologies has been security. Across healthcare, retail and public administration, establishments have started experimenting with blockchain to handle data to prevent hacking and data leaks. In healthcare, a technology such as blockchain can make sure that multiple "signatures" are sought at every level of data access.

Also, blockchain will give companies greater confidence in the integrity of the data they see. Immutable entries, consensus-driven time-stamping, audit trails and certainty about the origin of data are all areas where we will see improvement as blockchain technology becomes more mainstream. Longer term, we will very likely see a move from proprietary data silos to blockchain-enabled shared data layers. In the first era of BI, power resided with those who owned the data. In the blockchain era, power will reside with those who can access the most data and who can gain the most insights most rapidly. When data moves out of proprietary systems onto open blockchains, having the data itself is no longer a competitive advantage. Interpreting the data becomes the advantage.