The Trends of Smart Analytics in 2020: 2/2 – 5 Major Changes
March 31, 2022
This week, Zion Market Research reported that the smart grid technology market, which fuels energy-efficient smart cities, will be a $5.7B industry by 2025–and has produced a slew of academic conferences examining applications for 2020. For the average consumer, Artificial Intelligence-powered home products that employ data analysis to automatically adjust things like heating and cooling settings (or even optimize your laundry cycles!). Gartner has identified the manufacturing industry as one of the biggest beneficiaries this year, which is needed more than ever amid the current global health crisis. But what 2020 holds for business intelligence based on big data across other industries is a little more complicated, and lies in practice more so than in substantial technological developments.
Here are five areas where we see smart analytics ramping up in the next year:
#1: Businesses Will Stop Building.
We promise this is good news. It means companies will stop reinventing the wheel, learn from the vast domain knowledge of specialized companies, and leverage existing technologies rather than design entire systems from scratch. We’ve seen this mindset shift come to fruition in the last few years, and more companies opted for pre-built tools like AWS and Google Cloud rather than undertaking such costly repetition. In 2020, companies will realize that they don’t need to hire a team to build a bespoke reporting tool to show the value of their data and that the power instead lies in their ability to plan around a clear idea of what they’re looking for. Many companies already have prebuilt or canned solutions specifically tailored to their domain, especially B2C businesses like mobile phone gaming, entertainment, and e-commerce. Companie will save time and money, understanding that whenever they engage with an analytics tool or platform vendor, they are essentially borrowing experiences. (Kubit has done thisthemselves via our partnership with Snowflake).
#2: Data Will See a Reverse Osmosis.
As more policies stress user privacy, more limited access to data has experts saying now is the time to build a reliable benchmark with user data. A few years ago, that would have been challenging, but this year we expect to see a more natural progression toward businesses having the software come to them instead. In the earlier days of smart analytics, companies had to send their data to Tableau and Looker, on top of spending years building their own data warehouses and engineering pipelines to get things moving. Now they realize they need more smart automation, but that many of these new, “advanced” tools don’t work with their existing data warehouses. That’s why Kubit built its platform around bringing these new capabilities to businesses’ databases.
#3: More Meaningful Automation Evolves.
Smart data analysis is going beyond measurement into a more profound trend analysis paired with prescriptive diagnostics (which we call Augmented Analytics). Instead of building reports individually, AI will provide richer context–and the companies paying attention will jump on this trend before the rest. This trend will assign AI the tedious tasks that humans don’t need to do (and don’t like to do), like manual checking. Now, machine learning can automatically generate a suggested course of action when it sees a problem–while continuing to learn from human practices within the platform. We see this being especially useful in social media marketing.
#4: Data-Driven Decisions Become Cooperative.
Ever since businesses have started working with complicated data sets, most viewed it as a precise science only understandable to those with a Master’s Degree in data science–putting entire companies at the mercy of the highest-paid people in the room. Bottlenecks and misinterpretations became rampant, leading industries to look for an alternative route. This year, we predict more businesses will open their data, allowing most anyone to investigate the data behind market trends. To make whole-business participation work, companies will start employing user-friendly platforms to democratize data analysis in a way that streamlines cross-department participation using a single-source of truth.
#5: Data Quality Gets the Attention It Deserves.
The aunt that shares a fake news story she found on Facebook started out as a joke but has proven to be so damaging that it is now a worldwide debate around ethics. We already see a movement away from “any data” to “vetted data,” assisted by artificial intelligence and machine learning, to help users identify issues in the pipeline as well as provide detailed explanations. In 2020, we see technology as a conduit to help companies facilitate a dictionary of reference of what the data means today, what it meant six months ago, and what changed under what context (and why). It’s a proactive way to make decisions based on high-quality data with constant maintenance over data quality control. In the past few years, some of the biggest companies experienced how outdated statistics can degrade a query’s performance, no matter how refined the Search Query Language (SQL). For more on this particular problem, read our interview with Smule’s SVP of Analytics on the pitfalls of bad data practices like this one.