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Opinion piece by Datascope Analytics
Generally, there’s a lot of hubbub about trends in data and technology, often written from uninformed perspectives. We thought we’d weigh in on some of the data trends we think are important for executives to pay attention to throughout 2015, with links to background from people we trust.
Trend #1: IoT
What Is It? The Internet of Things (IoT) is the blanket term referring to everyday objects like refrigerators, coffee machines, and TVs gaining network connections that enable them to talk to each other via standard protocols (i.e. the internet). This is happening in the consumer world as well as in commercial and industrial applications. The IoT revolution that has long been discussed in computer science and engineering is becoming a reality thanks to a pervasive wireless Internet coupled with the falling size and cost of computing components.
What’s the Data Angle? Throughout the year we’ll see more and more internet-connected products and applications introduced in both consumer and commercial markets. We believe the most innovative ones won’t just collect data and send it back to users—they’ll combine the data they collect with other, disparate datasets, present it to people in compelling and useful ways, and even take actions automatically.
Data science will play an important role in helping new IoT devices and applications succeed, whether that’s by using machine learning techniques for autonomous decision-making, or by presenting data in informative ways to help humans better understand how to improve their lives and businesses. Think of products like the Nest thermostat or Dropcam, but on a larger scale—industrial applications that can help organizations monitor and change their operations in real time, to save money and detect problems early.
Where You Can Read More: Read Gartner Inc.’s recent survey exploring how much business leaders understand IoT and its potential business impact.
Trend #2: Q&A-Focused Analytics
What Is It? In recent years many businesses have concentrated on “big data” and “data warehousing” efforts. In order to get value from that stored data, people across the organization need to be able to ask questions of it easily, and receive answers that are relevant and actionable.
What’s the Data Angle? It’s important that analytics efforts within a company are designed around solving problems and not just providing instrumentation without a point. At Datascope, we wholeheartedly agree with the following Gartner guidance to businesses: “Big data remains an important enabler for this trend but the focus needs to shift to thinking about big questions and big answers first and big data second — the value is in the answers, not the data.” We’d also add that big questions can often be broken into smaller ones, in the service of making quick progress and iterating towards a more complete solution. Having concrete answers, even to small problems, helps employees ask bigger and better questions in the future, and injects excitement about data into the culture of the organization.
As businesses seek to build analytics capabilities internally, one important thing to remember is that “analytics” means different things to different audiences— the word is used indiscriminately to describe a variety of tools, processes, and solutions. Different companies and departments have different needs when it comes to data, and seeking well-designed, purpose-built tools that integrate nicely with existing practices ensures that data actually becomes involved in decision making across silos. For some, simple summary statistics like averages and conversion metrics will be enough, while others will desire more in depth analysis.
Where You Can Read More: Dive into the trends with CIO.com’s article on “Analytics Trends to Watch in 2015”, or read this insightful piece from John Foreman (Data Scientist for Mailchimp) about what kinds of data efforts make sense at a company with a rapidly changing business model.
Trend #3: Data Journalism
What Is It? This rising trend in journalism relies on data visualization and analysis to relate a story to audiences, and has grown in popularity in the last couple years. Noticing the success of this trend over the last year or two, the major news outlets are all investing resources in it, and big public relations firms and media companies tasked with generating traffic are giving it a shot too.
What’s the Data Angle? One risk audiences need to be aware of is that data journalism can be used to convey a false air of objectivity. It’s data, so it must be objective, right? At Datascope, we offer our unequivocal support to New York Times Senior Software Architect Jacob Harris’s warnings about the potential problems with data journalism and the need to approach data as skeptically as any other content from a journalistic source. To earn and retain the trust of audiences, media organizations using data in their reporting must transparently convey the sources, assumptions, and analysis related to the data presented.
For example, this blog written by two professors of statistics disproves many of the “data-based” claims made by media organizations about the New England Patriots in the wake of the now infamous “Ballghazi” investigation. Their analysis shows how easy it is for a writer to twist data into a story that isn’t there. As the professors demonstrate, audiences of data journalism will do well to remain skeptical, insist upon transparency, and study data collection and analysis methods to find publishers and journalists they can trust.
Where You Can Read More: Read what New York Times Senior Software Architect Jacob Harris has to say on why both journalists and audiences need to remain skeptical and learn to “distrust the data.”
Trend #4: Privacy and Security
What Is It? The challenges of data privacy and security affect individuals, businesses, organizations and governments alike. The question is big: How do we protect individual privacy and keep people and information safe and secure?
What’s the Data Angle? As prominent cases like the Sony leaks and Facebook’s experiments underscore, it’s important for business and tech leaders to develop thoughtful and deliberate analytical approaches that respect the privacy of customers and employees. The inexpensive cost of storing data is encouraging companies to hoard more and more information, even if they aren’t doing anything with it. While storage costs are lower than they’ve ever been, data breaches and successful de-anonymization efforts over the last year prove that data storage costs can be more than simply the cost of keeping it on a server. It has to include the high cost of protecting data and re-gaining customers’ trust if their data is abused or stolen.
Datascope advocates for companies to focus on data collection from a “solution-first” perspective. This means first designing promising use cases and then reverse-engineering them to determine what data truly needs to be collected and stored. This approach is especially important in cases where personal data is involved. In 2015 and beyond, the best data science solutions will be the ones that respect privacy as a design constraint rather than something to consider after the fact.
Where You Can Read More: Explore critical data privacy concerns in this conversation on de-anonymization in the New York Times and in this examination from MediaPost.com on corporate data storage practices.
All of the trends mentioned above contribute to another trend—soaring demand for data science talent. Datascope has seen a sharp rise in the number of organizations expressing interest in either 1) training existing analysts in modern data science techniques or 2) building internal data science teams from scratch. We’re also seeing many businesses working to spread data literacy across roles in the company so more employees can use data to better inform their daily decisions. The universality of these four trends coupled with soaring talent demand make it clear data science is rapidly becoming a mainstream business function. That may be the most notable 2015 trend of all.
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This article was written by Datascope Analytics
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