Like all technologies, big data is constantly evolving and the start of a new year is a good time to take stock, look for areas for improvement and seize new opportunities.

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2022 will be a pivotal year for big data, AI and analytics, with more and more companies expecting tangible business results. But from an IT perspective, there is still a lot of work to be done. Here are 10 Big Data New Year’s resolutions for IT.

1. Establish a data retention policy

Many organizations have just launched the box into the field, completely avoiding the big data retention discussion. This could be due to fear of what might be necessary if the company were forced to make a legal discovery for a lawsuit – but most likely, data retention is lacking because no one has taken the time for it.

With global data projected to increase to 180 zettabytes by 2025 And with big data making up 80% of that data, 2022 is the time to adopt big data retention policies and get rid of the data you don’t need.

SEE: Electronic data elimination policy (TechRepublic Premium)

2. Define the role of Big Data in the Data Fabric

To break down departmental system silos and make all of the organization’s data available for analysis and decision making, IT needs to focus on integrating big data as well as more traditional structured data into the data fabric it builds to connect all these silos and repositories.

3. Develop more low-code, no-code analysis applications

Implementing no-code, low-code reporting tools for analytics can get more analytics reports into the hands of end users faster, while relieving IT workload.

4. Re-evaluate the commercial value of the deployed applications

It’s great to launch an analytics app into production, but does it work as well for the business today as it did two years ago when it was first deployed? ?

Business is constantly changing. There is bound to be a “drift” between what analytics solutions continue to focus on and what the business needs now.

In 2022, it would be interesting to take a look at the efficiency of the analytics applications you have currently deployed to see how well they perform and if they still meet the needs of the business use cases they were designed for.

5. Develop an application and data maintenance strategy

As with data and structured applications, those that use big data and analytics also require maintenance. Yet many organizations deploying analytics and big data don’t have locked-in procedures in place for maintenance. Big data and analytics in production have reached a level of maturity where maintenance procedures need to be developed and put into practice.

SEE: Snowflake Data Warehouse Platform: A Quick Reference (Free PDF) (TechRepublic)

6. Improve computer skills

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 handling management, as well as proficiency with newer development tools, such as low-code and no-code analysis. -coded.

7. Examine 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 compliance with company security and privacy standards, as should your own internal big data.

8. Evaluate supplier support for big data and analysis

Many vendors offer tools for big data and analytics, but not all of them offer the same level of support when you need it. It is important to work with suppliers who To do offer active support to your staff in the use of big data and analytics tools, as well as advice on key projects. If you are working with vendors who do not offer the level of support you are looking for, it would be advisable to find vendors who do.

9. Improve big data and analytics that support the customer experience

Almost all businesses want to improve their customers’ experience with them. At the heart of this process is the development of customer-centric automation and aids to help customers get answers to their requests, questions and problems.

The automation of customer-centric systems (e.g. chat, phone attendants, etc.) that use NLP (natural language processing) and AI (artificial intelligence) to interpret customer feelings and initiate conversations is a long way off. to be mature.

Businesses 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 about big data and analytics began when both started to be implemented in organizations. Today, these technologies are more mature and are integrated into the general system of the company. 2022 is a good year for CIOs to come together again with other C-level leaders and stakeholders to recap the progress of AI and analytics and to gain their support for the next steps.

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