Top Business Intelligence Trends for 2018
Over the past decade, business intelligence has been revolutionized. Data will be more accessible than ever before. Read on the top 10 business intelligence trends for 2018!
Data is invaluable to all companies from budding startups to global enterprises. The ability to read,work and analyze data is more critical than ever in today’s analytics economy. Over the last few years, new trends have emerged that have had an enormous influence on how organizations work, interact, communicate, collaborate & protect themselves. For a competitive edge in 2018, organizations must recognize the strategies, technologies and business roles that can enhance their approach to business intelligence. Many organizations are moving towards Business Intelligence.
Here are some of the most critical trends to keep in mind looking ahead at 2018:
1 Do not fear AI (Artificial Intelligence) – How machine learning will enhance the analyst
Popular culture is fuelling a unpleasant view of what Artificial Intelligence can do. But while research and technology continues to improve, machine learning is rapidly becoming a valuable supplement for the analyst.
By automating simple, yet labor-intensive tasks like basic math, analysts gain time to think strategically about the business implications of their analysis and plan for the next steps. Secondly, it helps the analyst stay in the flow of their data. Without stopping to crunch numbers, analysts can ask the next questions to drill deeper.
Machine learning’s potential to aid an analyst is irresestible. But it is critical to recognize that it should be embraced when there are clearly defined outcomes. While there might be concern over being replaced, machine learning will supercharge analysts and make them more precise and impactful to the business. Machine learning helps you look under lots of rocks when you need assistance getting an answer.
You might question - why artificial intelligence will be so important? The answer is obvious. Businesses are going through data explosion. Every business creates more data than they can handle. But many of the businesses are struggling to turn that data into something useful. Artificial Intelligence is getting more powerful and more accessible than ever before. Just like that Internet of Things also continue to grow along with data explosion.
2 Natural Language Processing (NLP)
Natural Language Processing refers the way we interact with the AI through the UI. The main problem many companies face is that most of their employees do not have a technical background and have no idea how to query a piece of data. This is where NLP comes into play.
Gartner predicts that by 2020, 50% of analytical queries will be generated via search, NLP or voice. NLP will empower people to ask more distinctive questions of data and receive relevant answers that lead to better insights and decisions. Simultaneously, software developers and engineers will make greate progress in exploring how people use NLP by examining how people ask questions – from instant fulfilment to exploration.
The biggest analytic gains will come from tackling this uncertainity and understanding the diverse workflows that NLP augments. The opportunity will arise not from placing NLP in every situation, but making it available in the right workflows so it becomes second nature to those using it. NLP can open the analyst’s eyes a little bit and gives them some self-assurance and some confidence in what they are able to do.
3 The future of data governance is crowd sourced
It will be wrong to say that self-service analytics has disturbed business intelligence. The same disturbance is happening with governance. As self-service analytics expands, a funnel of valuable perspective and information inspires new and innovative ways to implement governance.
Governance is about using the wisdom of the crowd to get the right data to the right person as it is locking down data from the wrong person. Business Intelligence and analytics strategies will embrace the modern governance in 2018. Information Technology departments and data engineers will assist & prepare trusted data sources. After that self-service will be streamlined. So that end users will have freedom to explore the data. This data will be trusted and secured.
4 The debate for multi - cloud rages on
Big companies are desiring multi - cloud storage for their development. According to recent Gartner study, a multi-cloud strategy will become the common strategy for 70% enterprises by 2019. Right now the usage is less than 10% As enterprises grow increasingly wary about being tied to a single legacy solution, evaluating and implementing a multi-cloud environment can determine who provides the best performance and support for each situation.
Flexibility is a plus point. With multi-cloud adoption on the rise, companies must assess their strategy and measure adoption, internal usage, workload demands, and implementation costs for each platform.
5 The emergence of the Chief Data Officer (CDO)
Data and analytics are becoming core to every organization. But in some cases, a gap takes place between a CIO – Chief Information Officer and the business while battling security and governance versus speed to insight. With that, the C-suite is becoming more accountable for creating a culture of analytics. For many, the answer is appointing a Chief Data Officer (CDO) or Chief Analytics Officer (CAO) to lead business process change, overcome cultural barriers, and communicate the values of analytics at all levels. The role of CDO/CAO is outcome-focused. They ensure that there are proactive conversations happening about how to develop an analytical strategy. Ultimately, CDO’s role is to bring tools & technologies and empower the team.
6 The Location of Things will drive IoT Innovation
Location of Things is a subcategory of the (IoT) Internet of Things. It covers devices that sense and communicate their geographic location. Capturing this kind of data allows users to consider the added context of a device’s location when assessing activity and usage pattern. All companies have a concern about security, but most don’t have the right organizational skills or the internal technical infrastructure with other applications and platforms to solve their concern. Location of Things technology can be used to track assets, people and even interact with mobile devices like smartwatches or badges to provide more personalized experience. If data is available, analysts can incorporate this information to better understand what is happening, where is it happening and what they should expect to happen.
7 The Human impact of Liberal Arts in analytics industry
The focus on tech specialties is decreasing as technology platforms are becoming easier to use. Everyone can play with data without needing to have the deep technical skills once required. This is where people with broader skills, including liberal arts come into the fold and drive impact where industries and organizations have a data worker shortage. An increased focus and prioritization of data analytics will also put these data stewards in the position of helping their companies gain a competitive advantage. As analytics evolves to capture both art & science, the focus will shift from simply delivering the data to crafting data-driven stories that influence decisions.
8 Vulnerability leads to a rise in data insurance
Data is a critical business asset. According to 2017 study by the Ponemon Institute, the average total cost of a data breach was estimated approx $3.62 million. As we have seen with the recent high profile data breaches, a threat to a company’s data can be crippling, causing irreparable brand image. One solution is cyber and privacy insurance. It covers a business’s liability for a data breach in which the customer’s personal information is exposed or stolen by the hacker. Look for the companies to wisely invest in cybersecurity insurance to make sure your asset is protected.
9 Increased importance of the data engineer role
Data play major role while taking business decisions. Along with data data engineers are also an integral part of the business. Between 2013 & 2015, the number of data engineers increased more than doubled. As of October 2017, there were over 2,500 open positions with “data engineer” on Linked in, indicating the growing and continued demand for this specialty. Data engineers play a fundamental part in utlising the modern analytics problems.
As the rate of data and storage capacity increases, someone with deep technical knowledge of the systems, architecture, and the ability to understand what the business wants and needs become more crucial. That is where data engineers come on the stage. Data engineers are responsible for designing, building, and managing a business’s operational & analytical databases. They are also responsible for extracting data from the foundational systems of the business in a way that data can be used and leveraged to make right decisions. A 2016 Gartner study found that some organizations were losing an average of $9.7 million annually as a result of poor data quality.
10 Universities double-down on Data Science & Analytics programs
According to recent PwC study, 69% of employers by the year 2021 will demand data science and analytics skills from a job candidates. By Harvard Business Review, data scientist had been voted as “sexiest job of the 21st century”. The same PwC report stats that only 23% of college graduates will have the necessary skill set to compete at the level employers demand. The hard skills of analytics are no longer an elective; they are mandatory. 2018 will begin to see a more rigorous approach to making sure that students possess required data analytical skills to join the modern workforce.
North Carolina State University is home of the first Master of Science Analytics program. They are focusing on producing the world’s finest analytics practitioners who have mastered complex methods and tools for large-scale data modeling and who have a passion for solving challenging problems. Earlier this year, University of California, San Diego also launched a first of their institution – an undergraduate major and minor in data science.
Being data-driven is no longer an ideal; it has become necessity of the modern business world. The future of business intelligence is collaborative. 2018 will be an important year for business intelligence. Based on industry and internal requirements – organizations need to develop a business intelligence strategies for traditional analytics and advanced analytics using big data, data science, machine learning and artificial intelligence. If you would like to discuss more about these trends with us - feel free to contact us.