Like all technology, big data is continually evolving — and the start of a new year is a good time to take stock, seek areas of improvement and pursue new opportunities.
2022 will be a watershed year for big data, AI and analytics, with more companies expecting tangible business results. But from IT’s vantage point, there is still much work to be done. Here are 10 New Year’s big data resolutions for IT.
1. Establish a data retention policy
Many organizations have just kicked the can down the field, avoiding the big data retention discussion altogether. This could be out of fear of what might be needed if the company were compelled to do legal discovery for a lawsuit — but most likely, data retention is lacking because no one has made time for it.
With global data projected to grow to 180 zettabytes by 2025 and big data comprising 80% of that data, 2022 is the time to enact big data retention policies and to eliminate the data you don’t need.
SEE: Electronic Data Disposal Policy (TechRepublic Premium)
2. Define big data’s role in the data fabric
To break down departmental system silos and avail across-the-organization data to everyone for analytics and decision making, IT should focus on bringing big data as well as more traditional structured data into the data fabric it constructs to link up all of these silos and repositories.
3. Develop more no-code and low-code analytics applications
Implementing no-code and low-code reporting tools for analytics can put more analytics reports into the hands of end users faster, while bringing relief to the IT workload.
4. Reassess business value of deployed applications
It’s great to launch an analytics application into production, but is it working as well for the business now as it was two years ago when it was first deployed?
Business constantly changes. There is bound to be “drift” between what analytics solutions continue to focus on, and what the business needs now.
In 2022, it would be worthwhile to review the effectiveness of the analytics applications you currently have deployed to see how well they are performing and whether they are still meeting the needs of the business use cases they were designed for.
5. Develop an application and data maintenance strategy
As with structured data and applications, those employing big data and analytics also require maintenance. Yet many organizations deploying analytics and big data don’t have procedures locked in place for maintenance. Big data and analytics in production have reached a level of maturity where maintenance procedures should be developed and practiced.
SEE: Snowflake data warehouse platform: A cheat sheet (free PDF) (TechRepublic)
6. Upskill IT
To support big data operations and analytics, new IT skills are needed for staff. This may require additional training in data analysis, data science, big data storage and processing management, along with competency with newer development tools, such as low-code and no-code analytics.
7. Review security, privacy and trusted sources
Big data in particular can be acquired from a variety of third-party sources. These sources should be regularly reviewed for adherence to corporate security and privacy standards, as should your own internal big data.
8. Assess vendor support in big data and analytics
Many vendors offer tools for big data and analytics, but not all vendors offer the same degree of support when you need it. It’s important to work with vendors that do offer active support for your staff in the use of big data and analytics tools, as well as guidance during key projects. If you’re working with vendors that don’t offer the level of support you’re looking for, it would be advisable to find vendors that do.
9. Improve the big data and analytics that support the customer experience
Almost every company wants to improve the experience that its customers have with it. Central to this process is developing customer-facing automation and help aids for assisting customers in getting requests, questions and issues answered.
The automation of customer-facing systems (e.g., chat, phone attendants, etc.) that use NLP (natural language processing) and AI (artificial intelligence) to interpret customer sentiment and engage in conversations are far from mature.
Companies that focus on improving NLP and AI performance in these areas will benefit.
10. Renew big data and analytics discussions at the top
The first major discussions of big data and analytics began when both started to be implemented in organizations. Now these technologies are more mature and are moving into the corporate system mainstream. 2022 is a good year for CIOs to reconvene with other C-level executives and stakeholders to recap AI and analytics progress and to secure their support for next steps.