7 Trends for 2019 | Business Intelligence | Algorithms | Privacy

7 Business Intelligence & Big Data trends for 2019

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The top 7 BI trends for 2019

Data is becoming a bigger presence in our lives every day, whether we know it (or like it) or not. Naturally, this doesn’t just affect individuals, but businesses too. Smart use of data separates the winners from the losers. The most prominent Business Intelligence trends for 2019 are all about (use of) data, and above all: using data responsibly.

1. “Datacratic” working is becoming the norm

Datacratic working brings passion and joy back to the workplace. Connect data with continuous improvement and PDCA, and your organization will flourish. Becoming a true datacracy is the end goal that every organization should strive for. In this scenario, every decision will be supported by relevant data which is collected, stored, and then analyzed. This frees people from the drudgery of indecision and tyrannical managers who loudly proclaim their opinions while managing on gut feelings.

In a datacracy, all employees understand their role in the process and strategy of the organization. Datacracy is the next step in the evolution of organizations after agile working. However, many companies have a long way to go before they can embrace a datacratic culture. Structure, processes, and behavior will all have to change. And this change starts with the people.

2. Data literacy is an essential competency

Before datacratic working can become the norm, most people have a lot of catch-up to do with it comes to data literacy. Gartner goes so far as to call it an “extreme deficiency”, predicting that 80% of organizations will have started programs to promote data literacy among staff by 2020. This deficiency is holding back digital progress in many organizations, according to Ictivity (Dutch). According to them, a quarter of all managers in the Netherlands is annoyed by a lack of digital competency, and a third believes that the quality of their work suffers a result.

However, there’s always the risk that people overestimate their own skills while underestimating others’, when asked directly. This is known as the Dunning-Kruger effect. Only 8% of all managers thinks that they’re not data literate enough themselves. The fact remains that on average, there’s a lot of work to be done when it comes to data literacy. A large portion of managers also says they don’t have a good idea of how data literate their colleagues are, but this is also changing. Data literacy is becoming more and more measurable, which allows it to be used as a KPI. Improving data literacy will become a challenge for every organization.

3. Predictive analytics enters the mainstream

The predictive analytics market is going to be worth $12,41 billion by 2022, according to predictions. That’s 272% more than in 2017, a CAGR (compound annual growth rate) of 22,1%. The rise of AI and machine learning is causing a big growth in the predictive analytics market. The lion’s share of this growth is due to Pacific Asia, where the economy is continuing to boom.

Organizations are developing predictive diagnostic models by putting sensors on equipment. These sensors can identify potential problems early in order to perform predictive maintenance, saving money down the line.

4. AI humanizes analytics

The rise of algorithms, machine learning, and AI is causing some amount of anxiety, not entirely without reason. Are we losing our jobs to machines? Are our lives going to be dictated by cold algorithms? Nothing could be further from the truth. AI is still being programmed by people. Gartner has said that in 2019, AI will cause a net increase in jobs. It will create 2,3 million jobs, while making 1,8 million jobs redundant.

There’s still a huge gap between the amount of data being generated, and the capacity of people to process this data and take action based on it. That’s exactly what people are good at: analyzing complex problems in context, using intuition and empathy.

5. “Data for good” is on the rise

BI can be used for more than just improving margins and processes. Companies are putting more and more time and energy into Corporate Social Responsibility programs, projects aimed at creating a better world. For example, Ikea built a solar farm for 20,000 Syrian refugees in Jordan.

Naturally, projects like this aren’t purely altruistic. Research shows that millennials prefer working for companies that contribute to creating a better tomorrow. They’d even take a pay cut in exchange for a position in a company that takes its social responsibility seriously. There are also the so-called data commonwealths: platforms dedicated to sharing resources between various organizations to contribute to, for example, cancer prevention research. Data has to be in service of people, and big companies have to justify their actions.

6. Smart algorithms heat up privacy debate

The use of algorithm-driven technologies like big data, internet of things, and artificial intelligence is causing new legislative challenges. These technologies can affect the choices we make, and thus our personal autonomy. On top of that, algorithms can reflect the biases of their creators, leading to (unintentional, but very real) discrimination, such as algorithms that prefer male candidates over female applicants for a job, simply because the company has a history of hiring more men than women. Smart algorithms impact our privacy rights, equality, and procedural rights.

Data surveillance can breach our right to privacy. These breaches can occur in personal spaces as well as public ones, for example smart cities. The use of artificial intelligence, for example in healthcare, can also affect relational privacy. The average citizen is documented in hundreds of databases. These databases don’t just contain peoples’ digital footprints, but also their “data shadow”, which consists of all the information generated about them by third parties. This makes privacy protection a major issue.

7. New technology demands new legislation

2018 was the year of GDPR, the General Data Protection Regulation. 2019 is the year of the European ePrivacy regulation, among other things. This regulation is intended to provide better protection of digital communication, and includes rules about the use of emails, telemarketing, cookies, and other forms of electronic communication, such as Skype and WhatsApp.

In California, politicians and legislators have turned their attention on these matters as well. The California Consumer Privacy Act will affect the usual suspects, such as Facebook and Google, and is their equivalent to the GDPR.

Laws and “hard” rules aside, the Internal Organization for Standardization is also advocating norms for AI and Big Data. Their goal is to implement, test, and demonstrate the reliability, robustness, ethics, and legislative questions surrounding AI applications and big data. These norms will have to ensure that AI applications will be safe and serve people, society, and businesses.

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