Though the year is only half over, we’ve already witnessed several significant developments in the world of data and analytics. Whether you’re planning for next year or looking to improve your practice in the coming months, these 10 resolutions will help you steer a course in the right direction. We forecast that big data use will continue to grow rapidly in the coming year, so it’s important to set yourself up for success. Here are ten data and analytics resolutions for 2022.
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1. Prepare a data retention policy
Many companies have avoided the topic of big data retention altogether, instead of kicking the can down the road. It could be because they are afraid of the consequences if the company is sued. But most likely, the reason for not retaining data is that no one has the time or the resources to do so.
2. Define the role of big data 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. More low-code and no-code analytics apps are possible
Low-code and no-code reporting tools can be used to provide more analytics reports for end users quicker, and also relieve the IT burden.
4. Assess the business value of all deployed applications
It is great to put an analytics application in production. But, does it work as well now as it did two years ago?
The business world is constantly changing. There will always be a “drift” between the analytics solutions that continue to be used and the current business needs.
It would be worth reviewing the effectiveness of analytics applications that you have currently deployed in 2022 to determine if they are still meeting business needs.
5. Create an application maintenance and data management strategy
Like structured data applications and applications, big data and analytics require maintenance. Many organizations that use analytics and big data do not have a set of procedures for their maintenance. Analytics and big data in production are at a maturity point where it is necessary to develop and practice maintenance procedures.
6. Acquire more IT skills
New IT skills are required for staff to support big data operations. This could require additional training in big data analysis, data science and processing management.
7. Security, privacy, and trusted sources:
Many third-party sources can provide big data, especially for those who are looking to acquire it. These third-party sources should be reviewed regularly to ensure compliance with corporate privacy and security standards.
8. Check out the vendor support for big data and analytics
While many vendors offer tools for big-data and analytics, not all vendors provide the same level of support. It is important to partner with vendors that provide active support for your staff when using big data and analytics tools as well as guidance during major projects. It is a good idea to look for vendors who offer the support you need if you are working with vendors that do not provide it.
9. Enhance the customer experience through big data analytics
Every company strives to make customers feel better about their experience with them. This process involves developing customer-facing automation to assist customers with their questions, concerns and requests.
Automation of customer-facing systems, such as chat, telephone attendants, etc. That use NLP (natural-language processing) or AI (artificial Intelligence) to interpret customer sentiments and engage in conversation are far from mature.
Companies who focus on AI and NLP performance in these areas will reap the benefits.
10. At the top, renew big data and analytics conversations
When both big data and analytics were first implemented in organizations, the first major discussions about them began. These technologies are now more mature and mainstream in the corporate environment. CIOs should meet with C-level executives, stakeholders and other stakeholders in 2022 to review AI and analytics progress and secure their support for the next steps.