In the last decade, we’ve seen all kinds of revolutionary stuff in the world of analytics and business intelligence (BI). Data has exploded and immensely increased in size, access to the cloud has become easier, and spreadsheets have taken a backseat to more insightful, actionable data visualizations and interactive business dashboards.
Fast forward to 2019, and the revolution of the business intelligence tools landscape shows no signs of slowing down.
Today, advanced analytics isn’t just for analysts. Self-service analytics have democratized the big data product chain. BI strategies are also more customized than ever, and organizations of all sizes are no longer wondering whether they need access to BI analytics, but what solution suits their unique needs.
With the BI environment getting more complex each year, it’s becoming increasingly critical for companies to keep a keen eye on emerging trends and new technologies. Not only does that keep them ahead of the curve, but also puts organizations in pole positions to cope with market competition and digitalization.
Here are the top Business Intelligence trends to expect in 2020:
1. An edge, multi-cloud, hybrid continuum
Corporations are increasingly taking advantage of cloud computing, but the resultant centralization is too much and may lead to inflexibility, vendor lock-in, and more risk.
Through a post-modern analytics platform, businesses will centrally calibrate data and distribute it to several clouds, while simultaneously using a hybrid, continuum outlook that'll include edge computing.
2. Single view of all data will replace data centralization
In 2020, corporations will shift their focus from storing their data in a single location to creating a single view of all their data.
Data models will be more standardized, but the real game-changer will be the dawn of enterprise data catalogs. These will be accessible via a hub with a single view of all federated data estate. They'll also provide a shop-for-information marketplace experience.
3. The dawn of explainable AI
In recent years, Artificial Intelligence (AI) has been touted as the next big thing in BI trends. It has shown great promise in BI, particularly with the ability to augment human intelligence by injecting automation to the decision-making process. Gartner research predicts that by 2020, 85% of CIOs will be experimenting with AI programs via a mix of build, outsource, and buy efforts.
But as companies rely more on machine learning models, there’s an emerging concern that the recommendations provided by AI aren’t trustworthy and explainable. That’s because most of the machine learning applications currently lack a way to “look under the hood”, and users can’t understand the logic and/or algorithms behind recommendations and decisions. Throw in the cost of implementing AI, and machine learning models don’t look feasible to many small and medium-sized businesses.
This has created the need for explainable AI – one of the BI trends we expect to dominate the business tech environment in 2020. Explainable AI will allow decision-makers to make follow-up inquiries as to why an AI model recommends a particular decision, its level of confidence in the recommendation, and what it would recommend with different inputs.
The decision-makers will query the AI models in the same way they would if they were taking recommendations from a human expert. That, however, doesn't mean analytics and AI will completely replace human intelligence. Rather, it’ll augment human expertise. For businesses that need to be extremely agile in making critical decisions, explainable AI will be worth the investment.
4. Merging of visualization, presentation, and conversion technologies
The last five years have seen machine-driven data storytelling emerge, a trend that has allowed narrations via natural language generation. The addition of natural language processing and natural language query—also known as conversational analytics—will make machine-driven data storytelling more interactive, and more organizations will adopt this technology.
Throughout 2020, you can expect data presentation, data discovery, conversational analytics, and data storytelling technologies to gradually merge.
5. More focus on data governance
Data governance is the practice of exerting control over all data entries to ensure that they’re made according to pre-set standards. With more businesses realizing the value of data, you can expect more management and focus on data quality and governance like never before.
Data is only valuable when readily accessible. The problem is, too much access can sometimes compromise security. One of the most common challenges for most organizations in 2020 will be to strike a healthy balance between data security and access. There’s also the need to stay agile and adaptable to data and security changes in the business environment.
Additionally, corporations are realizing that data governance can foster a culture of business analytics and that people are much more likely to use a company’s data when the information sources are clean, centralized, and fast. This has created a need for self-service analytics.
However, organizations rushing to implement self-service analytics are facing Excel-like issues when it comes to data governance. As a result, many organizations will be looking to reinstate reliability and trust in their business analytics practices.
They’ll achieve this by creating more collaborative BI processes to get end-users and IT teams to agree on the choice of data governance models as well as their implementation. This will maximize the value of analytics to businesses without compromising on data security.
6. Platforms will gradually develop into systems
A post-modern business intelligence system will have a host of participants, each with different skills, roles, and intentions. Bots, extensions, digital services, algorithms, and intelligent agents will grow in complexity and diversity to make the system more valuable.
A self-learning, open system is the future of post-modern business intelligence and will enable both analytic empowerment and data democracy.
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