2018 Top 10 Business Intelligence Trends – Part 3
7- Data Insurance
Vulnerability Leads to a Rise in Data Insurance
For many companies, data is a critical business asset. But how do you measure the value of that data? And what happens when that data is lost or stolen? As we have seen with recent high profile data breaches, a threat to a company’s data can be crippling and potentially cause irreparable damage to the brand.
According to a 2017 study by the Ponemon Institute, the average total cost of a data breach was estimated at $3.62 million.
But are companies doing everything they can to protect and insure their data? One industry rapidly growing in response to data breaches is the cybersecurity insurance market. This industry has seen 30 percent year-over-year growth, with the industry set to reach $5.6 billion in annual gross written premium by 2020. (AON)
Cyber and privacy insurance covers a business’ liability for a data breach in which the customer’s personal information is exposed or stolen by a hacker.
However, even with the market’s growth and the continued threat of data breaches, only 15 percent of U.S. companies have an insurance policy that covers data breaches and cybersecurity. Furthermore, when you look at those 15 percent of U.S. companies covered, a majority come from large, established financial institutions.
The need for policies with financial institutions is clear. But the trend will broaden to other verticals because nobody is immune to the threat of a data breach.
Doug Laney, Gartner Analyst, recently wrote a book titled, “Infonomics: How to Monetize, Manage, and Measure Information for Competitive Advantage.” He gives distinct models on how companies across all industries can review the value of their data, both in non-financial models and financial models.
Non-financial models focus on the intrinsic value, the business value, and the performance value of the data. These values can measure a company’s uniqueness, accuracy, relevancy, internal efficiencies and overall impact on its usage.
Financial models focus on the cost value, the economic value, and the market value of the data. These values can measure the cost of acquiring data, administering the data internally, and the value of selling or licensing your data.
Data as a commodity means its value will only increase, and ultimately drive new questions and conversations around how this raw material will continue to project companies to greater heights and advantages. And like any product, what good is it if it can be pilfered without consequence?
8 – Data Engineer Role
Increased prominence of the data engineer role
Here is a certainty: you can’t create a dashboard without having all of your charts built out so you can understand the story you’re trying to communicate. Another principle you likely know: you can’t have a reliable data source without first understanding the type of data that goes into a system and how to get it out.
Data engineers will continue to be an integral part of an organization’s movement to use data to make better decisions about their business. Between 2013 and 2015, the number of data engineers more than doubled. And as of October 2017, there were over 2,500 open positions with “data engineer” in the title on LinkedIn, indicating the growing and continued demand for this specialty.
So what is this role and why is it so important? The data engineer is responsible for designing, building, and managing a business’s operational and analytics databases. In other words, they are responsible for extracting data from the foundational systems of the business in a way that can be used and leveraged to make insights and decisions. As the rate of data and storage capacity increases, someone with deep technical knowledge of the different systems, architecture, and the ability to understand what the business wants or needs starts to become ever more crucial.
Yet, the data engineer role requires a unique skillset. They need to understand the backend, what’s in the data, and how it can serve the business user. The data engineer also needs to develop technical solutions to make the data is usable.
In the words of Michael Ashe, Senior Recruiter for Tableau, “I’m no spring chicken. I’ve been in technical recruiting for over 17 years. And it’s no surprise that data and storage capacity has continued to grow—I’ve seen it happen in quantum leaps. The data will always need tweaking. Businesses need to plug into this role. They need to dive into specific data to make business decisions. The data engineer most definitely will continue to grow as a role.”
9 – Location of Things
The Location of Things will Drive IoT Innovation
It’s an understatement to say that the proliferation of the internet of things (IoT) has driven monumental growth in the number of connected devices we see in the world. All of these devices interact with each and capture data that is making a more connected experience. In fact, Gartner predicts that by 2020 the number of IoT devices available to consumers will more than double “with 20.4 billion IoT devices online.”
Even with this growth, the use cases and implementation of IoT data hasn’t followed the same desirable path. Companies have concerns about security, but most don’t have the right organizational skill sets or the internal technical infrastructure with other applications and platforms to support IoT data.
One positive trend we are seeing is the usage and benefits of leveraging location-based data with IoT devices. This subcategory, termed “location of things,” provides IoT devices with sensing and communicates their geographic position. By knowing where an IoT device is located, it allows us to add context, better understand what is happening and what we predict will happen in a specific location.
For companies and organizations seeking to capture this data collection, we are seeing different technologies being used. For example, hospitals, stores, and hotels have begun to use Bluetooth Low Energy (BLE) technology for indoor location services, which were typically difficult for GPS to provide contextual location. The technology can be used to track specific assets, people and even interact with mobile devices like smartwatches, badges or tags in order to provide personalized experiences.
As it relates to analyzing the data, location-based figures can be viewed as an input versus an output of results. If the data is available, analysts can incorporate this information with their analysis to better understand what is happening, where it is happening, and what they should expect to happen in a contextual area.
10 – Academics Investment
Universities Double Down on Data Science & Analytics Programs
North Carolina State University is home to the first Master of Science Analytics program. The MSA is housed within their Institute of Advanced Analytics (IAA), a data hub with the mission to “produce the world’s finest analytics practitioners—individuals who have mastered complex methods and tools for large-scale data modeling [and] who have a passion for solving challenging problems…” As the first of its type, the NC State program has foreshadowed academia’s pronounced investment in data science and analytics curriculum.
Earlier this year, the University of California, San Diego launched a first for their institution—an undergraduate major and minor in data science. They didn’t stop there. The university also made plans, supercharged by an alumnus donation, to create a data science institute. Following suit, UC Berkeley, UC Davis, and UC Santa Cruz have all increased their data science and analytics options for students, with demand exceeding expectations. But why?
According to a recent PwC study, 69 percent of employers by the year 2021 will demand data science and analytics skills from job candidates. In 2017, Glassdoor also reported that “data science,” for the second consecutive year, was a “top job.” As demand from employers grows, the urgency to fill a funnel of highly-skilled data fiends becomes more critical. But there’s a reality gap. The same PwC report cites that only 23 percent of college graduates will have the necessary skills to compete at the level employers demand. A recent MIT survey found that 40 percent of managers are having trouble hiring analytical talent.
The hard skills of analytics are no longer an elective; they are a mandate. 2018 will begin to see a more rigorous approach to making sure students possess the skills to join the modern workforce. And as companies continue to refine their data to extract the most value, the demand for a highly data-savvy workforce will exist — and grow.
Source: Tableau
