From headline-making cyber-physical systems and cloud computing to the internet of things (IoT), cognitive computing or artificial intelligence (AI), complementary and consequential emerging technologies are changing how businesses and institutions operate. From Industry 4.0 innovations to smart cities and supply chain optimization, technological breakthroughs are shaking foundational assumptions and facilitating enormous leaps forward.
The power and possibilities these new technologies unlock are based on one key unifying element: data. The ability to gather, manage, analyze and utilize vast amounts of data — discovering new connections and gleaning new insights — is a true game-changer across a wide range of applications. Powerful, sophisticated data analytics tools and strategies are no longer luxuries, but rather critical necessities for competitive brands and businesses in many industries.
What are the latest data analytics trends and developments? How do effective data analytics tools and tech work, where are the pain points, and how can professional partners help new adopters of data analytics tools get the most out of their investment?
The Story
The best new analytics solutions don’t just present data; they provide invaluable context and connections. When data tells a story it becomes information, and when information gets tested it becomes knowledge — knowledge that can sometimes tell the future (or at least make a smart prediction).
For users, that starts with zeroing in on critical metric(s). Once you measure it, you can move it. With complex omnichannel data sets, multiple metrics are connected, and small changes in one can impact another. Understanding those relationships is critical, which is why it’s not just about KPIs, but APIs (application programming interfaces).
New Synergies
Another exciting data analytics frontier is where new AI tools and tech are being utilized to supercharge data analysis and identify needles of insight in the vast haystack of raw data. This has accelerated in the last 18 months, with many more businesses exploring the potential of new AI-powered data analytics solutions.
Even businesses with robust data analytics infrastructures are recognizing the potential of AI-powered insights. National brands are using digital assistants based on cognitive computing and AI to expand their analytical capabilities and transform quality data into usable data. In fintech, banks investing in mobile apps are recognizing that they’re only as good as the quality of the data behind them.
Now You See It
One of the richest areas of data analytics innovation is data visualization. Accessible, user-friendly data dashboards can distill complex analytics into digestible and actionable information. The best examples provide insights in real time, in ways that enable users to identify critical trends and status updates at a glance. The growing emphasis on data visualization might seem tangential to the technology under the hood, but distilling and displaying data is not just an art and science unto itself, but an essential link in the data analytics chain. With data analytics, visibility is a prerequisite for utility. Data isn’t an abstraction, but, like the wind, you frequently can’t see it or gauge strength and direction until you see the moving branches of a tree or the size of the waves on a body of water.
Companies and decision-makers more mature in their data journey are being proactive in identifying advanced data analytics tools. Cutting-edge tech like advances in GPU systems enabling deep learning are helping take data science to new levels. Sophisticated algorithms can identify hidden patterns and look for connections and repetitive behaviors that provide significant predictive value. In other words, data is no longer just telling us what happened or what’s happening now — but what’s likely to happen next.
Reality Checks
For all the promise and progress of new data tools and techniques, providers of technology systems and services are increasingly navigating “perception roadblocks.” Because as analytics, AI and other new tools become more popular, some prospective adopters are discovering that the hype doesn’t always match the reality — and that despite the thrilling potential of these new tools and tech solutions, streamlined plug-and-play adoption is often elusive. Understandably, companies are ideally looking for generic solutions that can be integrated and utilized without a high level of technical expertise. But the technical and operational reality is usually more complicated. The potential is very real (and the results can be potentially dramatic) but getting to that point is rarely as easy as pushing a button.
This is why some of the most successful operators in this unique tech sector are finding success developing more comprehensive solutions, or offering a partnership package that includes both technology and the services necessary to integrate and optimize that technology.
Making It Happen
With that in mind, finding the right tech partner is critical for companies looking to upgrade their data analytics capabilities. Before aiming to be the bleeding edge machine learning algorithm, remember that you need to have the raw materials in place. Gather, integrate and organize the data.
Here are four other best practices:
1. Broaden Your Horizons
Keep an open mind. Don’t make assumptions or limit what you want from your data. Experts often find more insights in the data than customers imagine. Understanding where and how to unearth value amidst the complexity is essential.
2. Be Patient
Analyzing the nature of internal systems and data (which often includes a combination of structured and unstructured data) is a prerequisite for effectively integrating new data analytics tools. Get intimately familiar with their systems.
3. Start Small And Build
Regardless of whether the initial analytics ask is augmentation, automation or visualization, start with a small and specific goal before expanding. Getting a quick win can demonstrate value and begin building a foundation of trust and technical capacity with the potential for exciting new advances moving forward.
4. Avoid Overkill
More often than not we try to use the latest and greatest (and more computation power hungry) solutions to solve our data analytics challenges just to find out later that simpler algorithms could have achieved the same results. The start small principle can be applied to the technology as well.
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