“Big data” is more than a trendy catchphrase floating around office boardrooms and educational conferences. It’s a $189 billion industry that’s projected to balloon to $274.3 billion by 2022. It’s also a powerful tool that, leveraged correctly, can offer critical insights into a company’s operations.
Think of leveraging big data like getting an X-ray at the dentist’s office. It’s an objective look at your company’s inner workings, indicating what is — and isn’t — functioning. The clearer and more comprehensive the image, the better prepared you’ll be to seize opportunities.
With that said, big data is one of the most complex business domains. With limited time, which areas should you keep an eye on?
1. ETL
ETL — short for “extract, transform, and load” — is a process used to consolidate data from multiple sources into a single data warehouse. An ETL process “reads” data, shifts it to a format that’s easily analyzed, and stores it in your company’s own searchable data warehouse.
As data volumes grow, ETL will only become more important. Data formats will proliferate, making comparisons more difficult.
If you’re considering adopting an ETL tool this year, remember that integrations are key. If you use payment tools like Stripe, can your ETL tool extract that information? And if your team is short on tech talent, be sure your choice has Coding Solutions.
2. Augmented Analytics
Many business intelligence tools capable of streamlining data collection and crunching numbers require a lot of manual input. However, Harvard Business Review’s analytics team sees a shift ahead: Data analysis tools are automating more parts of the process.
But the fewer steps handled by humans in a big data analytics operation, the better. By using machine learning to prepare data for sharing and set parameters, augmented analytics tools reduce the amount of time leaders have to invest. It also improves the quality of the insights they’re able to extract.
3. Prescriptive Analytics
For years, businesses relied on advanced and predictive analytics to forecast areas like sales and expenses. But there’s a better way to get a look at what’s coming next: prescriptive analytics.
Prescriptive analytics tools don’t just predict events that may happen; they offer suggestions on what to do.
How do prescriptive analytics work? By using machine learning to play out likely scenarios, they help organizations make decisions on what to do in response.
A prescriptive analytics tool might, for example, suggest tweaks to a blog post. By considering how similar posts have fared in terms of searches and social shares, a system might recommend changes to improve the content’s chances of going viral.
4. Natural Language Processing
If you talk to Alexa, you’ve encountered natural language processing before. NLP is a form of artificial intelligence that helps computers understand and interpret human speech. And it’s the reason Alexa activates upon hearing your voice and reacts to your request.
In the business world, NLP algorithms power everything from chatbots to email filters to sentiment analysis tools. According to Gartner, half of analytical queries will be generated by NLP or search — or automation — by 2020.
Expect NLP’s business applications to expand rapidly. As a result, companies will use it to pull insights from customer service call transcripts, answer FAQs, and autonomously handle administrative work.
5. Edge Analytics
By 2025, 64 billion Internet of Things devices will exist around the globe. To manage the troves of data they collect, companies will use edge analytics.
Edge analytics differ from traditional analytics in one key way: They crunch the data within the sensor or device itself. They don’t wait for the data to be sent back to the cloud for analysis. This will be essential for self-optimizing IoT devices, particularly those with limited data connections.
Edge analytics will crop up in everything from oil derricks to jet engines. They’ll predict maintenance needs, provide machine-by-machine efficiency reports, and free up servers for other tasks.
6. Artificial Intelligence as a Service
Artificial intelligence is far from new to many enterprise leaders. But for many small and midsize business leaders, developing AI technology internally is impossible or prohibitively expensive.
For that reason, many AI experts expect an uptick in providers offering AI algorithms as a service. Domain-tailored algorithms, such as those for spotting sales opportunities, will emerge before models become more generally capable.
Big data technologies are amazingly capable already. And for small business leaders, they’ll only become more so. Automated and prescriptive analytics, along with the services and tools that go with them, will catapult companies of all sizes into the next industrial revolution which will allow the fabrics to operate commonly use machinery like industrial fans and more at a low cost.
