Over the past decade business intelligence has been revolutionized. Data exploded, and became big. We all gained access to the cloud. Spreadsheets finally took a backseat to actionable and insightful data visualizations and interactive business dashboards. The rise of self-service analytics democratized the data product chain. Suddenly advanced analytics wasn’t just for the analysts.
2017 was a particularly major year for the business intelligence industry. The trends we presented last year will continue to play out through 2018. But the BI landscape is evolving and the future of business intelligence is played now, with emerging trends to keep an eye on. In 2018, business intelligence strategies will become increasingly customized. Businesses of all sizes are no longer asking if they need increased access to business intelligence analytics but what is the best BI solution for their specific business. Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data story. 2018 will be the year of data quality management and embedded BI tools: clean and secure data combined with simple and powerful presentation. It will also be a year of multi-cloud strategies and artificial intelligence. We are excited to see what this new year will bring. Read on to see our top 10 business intelligence trends for 2018!
1) Artificial Intelligence
This is the trend number #1 chosen by Gartner in their 2018 Strategic Technology Trends report, and is also the top list of our business intelligence trends. Artificial intelligence (AI) is the science aiming to make machines execute what is usually done by complex human intelligence. Often seen as the highest foe friend of the human race in movies (Skynet in Terminator, The Machines of Matrix or the Master Control Program of Tron), AI is not yet on the verge to destroy us, in spite the legit warnings of some reputed scientists and tech-entrepreneurs.
While we work on programs to avoid such inconvenience, AI and machine learning are revolutionizing the way we interact with our analytics and data management.
We are evolving from static, passive reports of things that have already happened to proactive analytics with live dashboards helping businesses to see what is happening at every second and give alerts when something is not how it should be. Our solution at datapine includes an AI algorithm based on the most advanced neural networks, providing a high accuracy in anomaly detection as it learns from historical trends and patterns. That way, any unexpected event will be notified and will send you an alert. We have also developed during this past year 2017 a new feature called Insights, also AI-based, that fully analyzes your dataset automatically without needing an effort on your end. You simply choose the data source you want to analyze and the column/variable (for instance, Revenue) that our decision support system software should focus on. Then, calculations will be run and come back to you with growth/trends/forecast, value driver, key segments correlations, anomalies, and a what if analysis. That is an incredible time gain as what is usually handled by a data scientist will be performed by a tool, providing every business users with access to high-quality insights and a better understanding of their information, even without a strong IT background.
The demand for real-time data analysis tools is increasing and the arrival of the IoT (Internet of Things) is also bringing an uncountable amount of data, which will promote the statistics analysis and management at the top of the priorities list. However, businesses today want to go further and predictive analytics is another trend to be closely monitored, as we have seen above. Gartner predicts that more than half of all large organizations worldwide will use advanced analytics and algorithms built on them to be more competitive by 2018. AI will be at the heart of those algorithms that understand the data and can predict what is upcoming, and its deep learning will probably make the machines operate autonomously and take decision in the place of a real person. Such a change would mightily transform decision-making and managers will need to know how algorithms reach their conclusion and eventually adjust. Businesses will also have to decide whether (semi) automated decision-making should be in the hands of algorithms or not.
2) Predictive and Prescriptive Analytics Tools
Business analytics of tomorrow is focused on the future and tries to answer the questions: what will happen? How can we make it happen? Accordingly, predictive and prescriptive analytics are by far the most discussed analytics trends among the BI professionals.
Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. It’s an extension of data mining which refers only to past data. Predictive analytics includes estimated future data and therefore always includes the possibility of errors from its definition. Predictive analytics indicates what might happen in the future with an acceptable level of reliability, including a few alternative scenarios and risk assessment. Applied to business, predictive analytics is used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company.
Industries harness predictive analytics in different ways. Airlines use it to decide how many tickets to sell at each price for a flight. Hotels try to predict the number of guests they can expect on any given night in order to adjust prices to maximize occupancy and increase revenue. Marketers determine customer responses or purchases and set up cross-sell opportunities, whereas bankers use it to generate a credit score – the number generated by a predictive model that incorporates all of the data relevant to a person’s creditworthiness.
Among different predictive analytics methods, two attracted recently the most publicity – Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA).
In Artificial Neural Networks data is being processed in a similar way like in biological neurons. Technology duplicates biology: information flows into the mathematical neuron, is processed by it and the results flow out. This single process becomes a mathematical formula that is repeated multiple times. As in the human brain, the power of neural networks lies in their capability to connect sets of neurons together in layers and create a multidimensional network. The input to the second layer is from the output of the first layer, and the situation repeats itself with every layer. This procedure allows for capturing associations or discovering regularities within a set of patterns with the considerable volume, number of variables or diversity of the data. ARIMA is a model used for time series analysis that applies data from the past to model the existing data and make predictions about the future. The analysis includes inspection of the autocorrelations – comparing how the current data values depend on past values – especially choosing how many steps into the past should be taken into consideration when making predictions. Each part of ARIMA takes care of different side of model creation – autoregressive part (AR) tries to estimate current value by considering the previous one. Any difference between predicted data and real value are used by the moving average (MA) part. We can check if these values are normal, random and stationary – with constant variation. Any deviations in these points can bring insight into the data series behavior, predicting new anomalies or helping to discover underlying patterns not visible by bare eye. ARIMA techniques are complex and drawing conclusions from the results may not be as straight forward as for more basic statistical analysis approaches. But once the basic principles are grasped, the ARIMA provides very powerful tool for predictive analysis.
Prescriptive analytics goes a step further into the future. It examines data or content to determine what decisions should be made and what steps taken to achieve an intended goal. It is characterized by techniques such as graph analysis, simulation, complex event processing, neural networks, recommendation engines, heuristics, and machine learning. Prescriptive analytics tries to see what the effect of future decisions will be in order to adjust the decisions before they are actually made. This improves decision-making a lot, as future outcomes are taken into consideration in the prediction. Prescriptive analytics can help you optimize scheduling, production, inventory and supply chain design to deliver what your customers want in the most optimized way.
3) Natural Language Processing
This trend is highly related to the previous business intelligence trends mentioned. Natural Language Processing, NLP or computational linguistics, is a branch of artificial intelligence related to the understanding of human language(s) by a computer program. It is based on linguistics and deep learning, a type of AI that works with pattern recognition to improve the program’s understanding by analyzing massive amount of data to find correlations that are relevant. Deep learning is a more intuitive and flexible approach that learns how to identify a speaker’s intents, a bit like when a child learns how to speak with his/her environment.
The technology underlying this human-computer relationship and understanding is changing our society dramatically. We can already see the applications with virtual assistants like Siri, Cortana or Alexa, or the incredible development of customer service chatbots that can help and answer clients more accurately every day.
That will also transform the way we do business intelligence in a future nearer than we probably imagine. Not only will the interface be changed, but the way we interact with it. As we saw in our Analytics and Business Intelligence Buzzwords for 2018, automation is growing and will take a big part in the future of BI. Within two years, 40% of data science tasks will be automated according to Gartner. Applying AI to business intelligence, and more particularly introducing NLP in business analysis tools will transfigure the entry barrier to BI by lowering or removing it entirely, and truly democratize data. Imagine: you are no data scientist, nor an IT professional, but you need to work on your humongous amounts of data gathered on many different databases that are centralized in one place. And you can do that by simply… asking the software your business questions. Orally. Like you would ask your colleague. Like you would ask yourself before digging in, going through, drilling down the mountains of data, finding the valuable information, organizing it, and then visualizing it thanks to modern dashboards. With a simple oral interaction with a BI chatbot, all this time and efforts would be spared, might also be less biased and more accurate than with a human.
Applying natural language processing to business intelligence would let you spend more time on more critical tasks where the human cannot (yet?) be removed, like actually asking the right data analysis questions, or elaborating the company’s business intelligence strategy. With NLP, you won’t just ask the question in natural language… you will also receive the response in natural language.
However, several challenges remain for NLP: speech recognition, natural language recognition and generation. The advances in NLP will help business analyze and learn from a greater range of data sources, at a faster pace, and thus increase productivity and give them a competitive advantage.
4) Data Quality Management (DQM)
The analytics trends in data quality grew greatly this past year. The development of business intelligence to analyze and extract value from the countless sources of data that we gather at a high scale, brought alongside a bunch of errors and low-quality in reports: the disparity of data sources and data types added some more complexity to the process.
Today, most companies understand the impact of data quality on analysis and further decision-making, and hence choose to implement a Data Quality Management (DQM) policy, department, or techniques. DQM is indeed reckoned as the key factor to an efficient data analysis, as it is the basis from where all the rest starts from. Low-quality data is estimated to cost over $600 billion a year to US businesses. The consequences of bad data quality are numerous from the accuracy of understanding your customers to making the right business decisions.
DQM consist of acquiring the data, implementing advanced data processes, distributing the data effectively and managing oversight data. We detailed the benefits and costs of good or bad quality data in our previous article on data quality management, where you can read the five important pillars to follow.
Data Quality Management is not only uprising in the BI trends 2018, but also a crucial practice to adopt by companies for the sake of their initial investments. Meeting strict data quality levels also meets the standards of recent compliance regulations and demands. By implementing company-wide data quality processes, organizations improve their ability to leverage business intelligence, and gain thus a competitive advantage that allows them to maximize their returns on BI investment.
5) The Multi-Cloud Strategy
The ubiquity of cloud is nothing new for anybody who stays up-to-date with BI trends. In 2018 the cloud will continue its reign with more and more companies moving towards it as a result of the proliferation of cloud-based tools available on the market. Moreover, entrepreneurs will learn how to embrace the power of cloud analytics, where most of the elements – data sources, data models, processing applications, computing power, analytic models and data storage – are located in the cloud.
There are more and more organization moving their data and all of their applications to the cloud. Gartner states that by 2019, the cloud will be the common strategy for 70% of the companies – while it was less than 10% in 2016. When evaluating hosting environment, you take risk, speed, costs and complexity into account, which makes it even harder to pick one solution fitting all your needs.
Opting for a multi-cloud strategy is then an option as it reduces risk and provides more flexibility – but such flexibility comes with certain costs, as you need several providers as well as training your teams to learn multiple platforms. Besides, you decrease your discount potential buy buying more, in a smaller quantity, and thus at a higher price.
This is why multi-cloud is a debatable choice within companies, even though its adoption is clearly on the rise. Business will need to assess their needs and capacities of implementation, to evaluate whether it would be beneficial and profitable to go for a multi-cloud strategy.
6) Data Governance
According to the DGI (Data Governance Institute), data governance is “the exercise of decision-making and authority for data-related matters.” In other words, it is the control over any data entry that has to be made accordingly to particular standards. Data, access, and security issues don’t all deal with data breaches. In 2018, organizations will increase focus on data governance and data quality. As data is only useful when accessible, organizations will increasingly look to strike a balance between data access and security. They also must learn to remain agile and adapt as the business changes.
New data preparation tools and methods will help fuel this trend and decrease the cultural gap between business and technology. Organizations are learning that data governance can help nurture a culture of analytics and meet business needs. Also, people are more likely to dig into their data when they have centralized, clean, and fast data sources.
The rush to implement self-service business intelligence capabilities has led to major Excel-like governance issues for a lot of organizations. In 2018, organizations will look to reinstate trust and reliability back into analytics practices. More collaborative processes will be created to help both IT teams and end-users agree and implement modern data governance models, maximizing the business value of analytics while not jeopardizing security.
Security is without doubt one of the biggest business intelligence trends in the last years. The news seem to be filled with reports of data breaches and database security issues, including huge data losses by big brands such AOL, MySpace, Compass Bank, AT&T, NHS, LinkedIn, Apple, JP Morgan Chase, or Uber. While the big companies make the news, concerns are also being raised over the vulnerability of small businesses.
Database security has become a hot debate, both in the public and in private organizations. This will only pick up speed in 2018. Business owners will increasingly search for the most secure solution that averts the risk of data breach and losses.
In this context a usually hot debate is the decision between on-premises or cloud BI tools: whether the software is installed locally in the company’s own servers, or if the solution is hosted in the cloud. At datapine, we support both options, and provided an analysis on the cloud vs on premise comparison, which can be summarized with the chart below.
8) Growing Importance of The CDO
Our eighth craze is cited in many other BI trends for 2018. We can safely affirm that today, data and analytics are getting core to every business. Every company has had a Chief Information Officer who supervised all the information management assets and security issues. But today, the data and analytics volume and role are getting so big that a new position emerged: the CDO, or Chief Data Officer, assigned to data management only. Gartner even says that 90% of large companies will have a CDO role by 2019.
A Chief Data Officer’s role is to create a system developed in a way that data can be leveraged across any business units within a company; from marketing to sales, to procurement, to finance. A CDO needs to empower all the users with trusted, clean, and ready-to-use data. They have to ensure that value can be extracted, and are hence outcome-focused.
The role of a CDO is essential for the good management of the information assets a company has, as well as to improve efficiency in the data analyses to get a competitive advantage in their BI strategy.
9) Embedded Business Intelligence
Second last of our business intelligence trends, embedded BI refers to the integration of a BI tool or selected features, into another business application to fill the gaps in the application’s analytics or reporting functionality. Thanks to it, you can turn raw data into interactive dashboards, enhancing the user experience with real-time analytics and innovative data visualizations, enabling people to make data-driven decisions faster and on their own.
These capabilities may be located outside of the application, but they have to be easily accessible from inside the app so that the user doesn’t have to alternate between systems and become accustomed to another user interface and structure. This way, embedded dashboards add features that are usually specific to BI software, enriching the application and making things simple for the user who also won’t need to install or adapt to a new tool. The time between collection of data and analysis of it is also shortened.
Today, collecting data has become easier than ever before, but some critics often say that by the time the business users would get the reports/dashboards, it would already be too late to undertake any action. This is where embedded BI steps in, facilitating in dealing and addressing that issue in shifting from reactive analytics to proactive analytics.
At datapine, our Authentication and Value Communication Module (AVCM) eases and speeds up the complex process of displaying only the relevant content for the user and restricting the access to the only data the user is allowed to see. Such module will enable you to deliver user-specific content on all your embedded dashboards, which can themselves be styled and re-branded so as to have the same aspect as your current application.
Let’s now tackle the last of our BI and analytics trends 2018!
10) Collaborative Business Intelligence
Last but not least of our business intelligence trends, is a topic that is important to us: collaboration. Today, managers and workers need to interact differently as they face an always-more competitive environment. More and more, we see a new kind of business intelligence rising: the collaborative BI. It is a combination of collaboration tools, including social media and other 2.0 technologies, with online BI tools. This is developed in a context of enhanced collaboration addressing the new challenges the fast-track business provides, where more analyses are done and reports edited. When talking about collaborative BI, the term “self-service BI” quickly pops up in the sense that those self-service BI tools do not require an IT team to access, interpret and understand all the data.
These BI tools make the sharing easier in generating automated reports that can be scheduled at specific times and to specific people for instance; they enable you to set up intelligent alerts, share public or embedded dashboards with a flexible level of interactivity. All these possibilities are accessible on all devices which enhances the decision-making and problem-solving processes.
Collaborative information, information enhancement and collaborative decision-making are the key focus of new BI tools. But collaborative BI does not only remain around some documents exchanges or updates. It has to track the various progress of meetings, calls, emails exchanges and ideas collection. As the founder of 9sight consulting Barry Devlin says, “It is much more than sharing the results from a particular BI tool; it’s about sharing the set of information that is being gathered within a team”. The future of business intelligence is collaborative.
What Are The Analytics & Business Intelligence Trends For 2018?
We’ve summed up in this article what the close future of business intelligence looks like for us. Here is the top 10 analytics and business intelligence trends we will talk about next year:
- Artificial Intelligence
- Predictive and Prescriptive Analytics Tools
- Natural Language Processing
- Data Quality Management
- The Multi-Cloud Strategy
- Data Governance
- Growing Importance of the CDO
- Embedded Business Intelligence
- Collaborative Business Intelligence
Become data-driven in 2018!
Being data driven is no longer an ideal; it is an expectation in the modern business world. 2018 will be an exciting year of looking past all the hype and moving towards to extract the maximum value from state-of-the-art online reporting software.