Last year was … interesting. While we couldn’t have predicted all that 2020 threw at us (and it was a lot), that won’t stop us from trying again this year, especially in technology.
Big data, like all technology, is evolving, so every year brings new opportunities and challenges. Here are some changes I’d like to see in 2021.
SEE: Report: SMB’s unprepared to tackle data privacy (TechRepublic Premium)
1. IoT standardization
The federal government passed legislation in December 2020 that requires Internet of Things (IoT) contractors to provide IoT security that conforms to specific government guidelines. This will drive IoT vendors to standardize their device security, a first step toward standardizing other elements of IoT, such as the diverse set of operating systems.
SEE: IoT standards: The US government must create them, and businesses will follow (TechRepublic)
Until IoT device makers and solution providers attain standardization, it will be difficult for their customer companies to achieve full integration in their IoT networks.
2. Stronger business use cases
There are many guides available that describe how to develop a business use case, but what if you can’t find a good business use case to begin with?
Use cases for big data have to do two things:
- They have to define a specific business problem that was unsolvable before, but that could be solvable and deliver benefits if big data and analytics can solve the problem.
- The use case has to return measurable and tangible value.
SEE: 7 on-the-ground big data strategies for 2021 (Techrepublic)
This isn’t always easy. For example, you could be asked by accounting to develop a trend analysis that addresses one small “need to know” area and that improves internal operations—but which fails to deliver much value to the company overall.
The questions that IT and business users should ask themselves before launching a big data project are:
- Does the use case deliver improved revenues, reduced costs, or something else that management considers highly valuable?
- What is the likelihood that the project will succeed?
3. Purposeful digital transformation
We are still at a point where too many organizations consider digitalization a success once they have digitalized information and stored it. But until you actively start integrating and using these new troves of digital assets with other enterprise IT, you’re not delivering enough benefit to the business from your digitalization.
SEE: Digital transformation: 3 things your organization can’t afford to overlook (TechRepublic)
In 2021, it will be important for organizations to begin leveraging their digital data by integrating and using it with other corporate systems.
4. More customer sensitivity
Using big data and analytics to predict customer preferences and then pitching products to customers has been a resounding success in retail and other industries. But when do customers reach a point where enough is enough?
There are some signs now that customers want to feel that they have some privacy as well as preemptive outreach from companies in the form of emails, text messages, phone calls, and web pitches.
2021 is the year when companies should begin to determine the sweet spot for welcome recommendations and responses to customers without crossing the line into personal privacy.
5. Better security
The cyberattack and theft of data from numerous government agencies in 2020 has raised awareness of a new generation of security vulnerabilities. No longer are network surveillance, endpoint monitoring, and malware detection on workstations adequate. Now, bad actors can come through a security update from a trusted vendor.
It’s a security wakeup call for big and standard data stewards—and time for IT to review its security and governance measures for all types of incoming data.
Security checks should be run against large data files of videos, images, and voice recordings; and software audits and certifications of security updates should be reviewed with vendors. Equally important is meeting on security with sellers of third-party data that you purchase for your analytics.
6. Eliminating noise
Big data is full of noise that means absolutely nothing to analytics and causes excessive processing and storage overhead. Noise can come in the form of network and device handshakes that you don’t want in your data or extraneous data that your use cases don’t require.
To reduce noise and optimize processing and storage, companies should eliminate extraneous data upfront.
7. Understanding ML, AI and NLP
Vendors are incorporating machine learning (ML), natural language processing (NLP), artificial intelligence (AI) and neural networks into their solutions, but many of these underlying technologies and algorithms are not well understood by their customers.
Vendors need to do a better job of educating customers in these areas.