Future of Business Intelligence - Trends and Tools to Handle Data Insights
To see the future of business intelligence, we must first accept that business intelligence is the future.
Neglecting the power of data is no longer an option; instead, businesses must get ahead of the tools and trends that can unlock bigger and better opportunities for growth and profit.
Business intelligence is best understood as information that guides business decision-making. The future of business intelligence, therefore, lies in increasing the value and usability of this information. In a world that is so heavily saturated in big data, this information has never been so widely available.
Business leaders are now able to make smart decisions, guided by expert, accurate data, to a level that is unprecedented. But business intelligence does not stop at data collection; it includes the transformation of this data, the presentation of data analysis to people within a business, and the interpretation of this data when making plans or evaluating business processes.
As such, business intelligence continues to evolve, and so does its usefulness. Companies that invest in this kind of data processing will see results that will propel them ahead of their competitors, help them tap into the lives of their target market, and guide them in designing a data-driven business that is prepared for success.
Business intelligence is changing the way business works
When we talk about how companies and corporations now approach the leveraging of data in their work, we have to first discuss what the data looked like before. For example, we’ve all seen the old movies about Wall Street, in which tons of papers surround employees. The papers are there because all data had to be collected, transformed, and analyzed manually.
Since then, a lot has changed - most companies keep their data in digital data warehouses instead of on paper. Computing allows faster and more efficient data transformation, but it also increases every company’s data capacity and signifies a move away from purely historical data. With a new focus on big data, we can collect more, understand more, and use these actionable insights to improve business performance.
With the current vastness of data collection, depth, and storage, it would be impossible to transform it manually, in the same way as before. It would require hiring a great number of people just to store the data, not to mention analyzing and ordering it. That’s why the future of business processes lies in digital acceleration. Speaking of data specifically, the answer to this problem is offered by business intelligence solutions (BI).
At present, many companies have already switched their analytical solutions to business intelligence (take a look at our Clark case study). Those companies are better informed by their data, which means that they have greater visibility over their data, and can make smarter, more successful business decisions based on their data.
They can also perform more accurate analysis of data, which leads to improved forecasting of trends and better planning of inbound and outbound processes.
Business intelligence also increases their competitive advantage. Companies that have implemented BI solutions notice crucial outcomes and opportunities before their more traditional competitors do. BI-driven companies may also expect an increase of revenue and cost reduction thanks to process automation and extended data analysis.
On top of that business intelligence can:
- Help business leaders evaluate performance against competitors
- Let you identify changes in customer behavior
- Measure changes in market conditions
- Forecast future probabilities for customer behavior and supply/demand
In the future, we can expect that companies of all sizes (including start-ups and SMBs) will invest more in their data environment, as data becomes a more intrinsic factor in gaining an advantage over market share competitors.
They will also invest in machine learning, artificial intelligence, and automation, which are set to improve exponentially over time. Those, BI, and other data science technologies can form a powerful tech combination, however they shouldn't be treated as synonyms, as each technology offers a different value to businesses.
How is business intelligence evolving?
Business intelligence has existed for some time now, and has already evolved. Its usefulness varies depending on how advanced it is for a particular company. Long-standing BI implementation and better knowledge of what the data looks like, how it can benefit the company, result in vastly improved data literacy and more widespread data-driven decision-making. These companies will also be better prepared and more open to further BI-based acceleration.
With more and more data sources, we can not only reliably transform our data but also gather the data from varied public data sources (such as weather and stock markets), and use it to enrich our data analyses with additional factors. As a result, BI is useful for much more than simply tracking sales patterns.
It can inform every aspect of a business and lead to:
- Marketing campaigns that are sharply targeted at different groups
- Better customer service
- Product design that reflects gaps in the market and customer interest
- More efficient business processes
- A reduction in business costs
- An ability to handle big data and unstructured data
- More careful handling of data as a valuable resource
With the evolution of business intelligence has come the development of more effective tools. We can now analyze data through more suitable platforms, oversee data quality management, connect it with machine learning models, and use advanced algorithms with just one tool.
So, how is the business intelligence industry changing now?
Embedded analytics is one of these change-drivers focused on making data analytics intuitive. Many companies decide to embed BI visualizations into their applications, which allows the user to seamlessly use analytics without the need to log into a separate tool.
Visualizations are adjusted to suit the company user interface and to reflect the key priorities that are most crucial to day-to-day processes. Making smarter, more informed decisions becomes second nature at every level of the company.
Many tools are based on cloud environments, in which all the data is stored, transformed and visualized on the one-stop-shop cloud platform. This is because it requires less on-premise infrastructure, and allows businesses to pick and choose the tools and data storage they need. Adding new tools and increasing data storage can be done almost instantly, and users can connect to and utilize new data sources quickly.
The agility of cloud solutions means little effort in connecting the right information to the right people across different devices and platforms. Hence, remote users stay connected with ease and businesses can bypass traditional software barriers.
It facilitates self-service BI and self-service analytics for users at all levels to access real-time data and BI insights, simplifying around-the-clock decision-making. Cloud-based BI can also be scaled as the company grows, and introduces new products, campaigns, or data sources.
Thanks to the fast development of cloud computing solutions across the digital acceleration realm, cloud BI now offers businesses the same processing power, customizable tools, and capabilities as infrastructure-heavy on-premise solutions.
The most advanced BI tools provide NLP (Natural Language Processing) Querying, which allows users to input natural language queries, which are processed by artificial intelligence (AI) algorithms to provide an accurate answer. The business intelligence part of this allows quick insights without much data expertise needed from the user. BI specialists are no longer needed for run-of-the-mill queries and can focus their attention and resources on business-critical planning.
NLP Querying is the logical companion to self-service BI. Rather than shoring off data warehouses from the average business user, NLP Querying uses APIs to make data more accessible via a search-engine type user interface.
AI technology has long been recognized as a disruptive factor in data processing, and NLP Querying is just one example of how this disruption continues to open up the world of data to more business departments. While there are still limitations on the types of queries that are recognized by BI NLP systems, movements have been made towards taking sentiment and emotion into account when searching data resources via voice recognition software.
Will business intelligence be automated?
With the rise of technologies like AI and machine learning (ML), it is safe to assume that their automation capabilities will be expanded upon and future BI trends will rely heavily upon them. However, like in most industries, many aspects of BI still require a human touch and interpretation.
We can rely on AI or ML to perform some basic analyses for us at the touch of a button, such as presenting trends or basic KPIs, but for more advanced tasks we need specialists like business analysts, BI engineers, and data scientists.
Some BI processes can already be automated. We can schedule data pipelines to run at a given condition, gather data from sensors or public data sources, and detect anomalies and outliers using advanced machine learning algorithms.
But, in the future, actionable business analytics will be even easier to access, thanks to automation. Less manual work will be needed to bring data together with BI tools, and automated algorithms will make these insights digestible via user-friendly dashboards.
It is conceivable that soon every business user will be able to draw insights that were once only intelligible to data scientists, and only the most complex data processes will be reserved for the experts.
Can BI predict the future?
Many people think that with the growing possibilities of machine learning and AI, we will have the possibility to predict the future. We can currently use BI insights to predict changes in market trends; but, the truth is that the future is always changing. Even with sophisticated data analysis, it is impossible to say for sure what will happen.
Business intelligence technology and machine learning give us the ability to predict trends and changes only by presenting us with data and insights that then have to be validated by a human user. Conclusions still have to be drawn through discussion and interpretation.
Predictive analytics gives us plenty of useful information, but does not provide certainty. Instead, BI is still best understood as crucial knowledge that can be used to influence business decisions, justify predictions, and help the business prepare for likely future outcomes.
What does this mean for the future of business intelligence?
The future of the business intelligence industry is dependent on the growth of business intelligence tools. With more effective machine learning algorithms in development, we will be able to do more with company data. As a result, companies implementing BI techniques will be able to access a full helicopter view of the company, with all factors affecting operations made visible.
With the growth of business intelligence and data literacy itself, we will be able to reach for more advanced analysis and KPIs. Analyzing and interpreting data will become an even more highly regarded and well-understood tool for business development.
In the future, we can expect an expansion of the data-oriented approach, with all of its benefits. This includes better integration of BI tools with the company’s applications, making business intelligence more accessible than ever for all business users.