Predictions for 2023: Data Analytics, Data Management, and DevOps Challenges and Strategies
Data management, data analytics and DevOps strategies to overcome upcoming challenges.
As companies continue their recovery from the global pandemic and prepare for the economic uncertainty of 2023, it seems clear that data will increasingly become a key part of their decision-making. This shift toward a more data-driven approach to business operations has already been in play for some years now. However, recent events such as the pandemic and a slowing global economy have dramatically increased the pace of change in almost every industry sector, discusses Nandan Umarji, co-founder and principal consultant, Mactores
For companies of all sizes, embracing these technological and policy approach changes will be crucial for staying competitive, particularly for those companies that have had to reduce their workforces and find ways to do more with less. These changes will undoubtedly come with many challenges, but the silver lining is that there will also be opportunities.
To aid in that journey, here are some top predictions for data use in 2023. Whether you’re a C-level manager or an IT team member, these are the changes in data analytics and data-driven management that you can expect as your company moves through the new year.
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More Companies Will Embrace Automated Data Analytics
Companies are increasingly turning to big data to carry out a variety of tasks, such as designing market projection models, identifying patterns for growth, and creating new products or services for their customer base. Even so, many companies still rely on manual data analytics processes to power these big data tasks.
As we move into 2023, we can expect to see more companies switch to automated data analytics, a system that uses advanced computer programs and simulations to discover and analyze digital information with little or no human intervention. Compared to a manual data analytics process, an automated system has a far greater degree of speed, accuracy, and efficiency, especially when it comes to handling large amounts of data on a day-to-day basis.
Embracing this change will be crucial for growing businesses dealing with ever larger amounts of data that are too much for any person – or even a team – to analyze effectively. Companies are well advised to get started sooner rather than later on implementing an automated analytics system as it can take a lot of time to switch over from a manual system of spreadsheets and plain-text files.
Greater Collaboration Between Business Leaders And It Teams
IT teams play a vital part in helping companies stay up to date on the latest evolutions in technology. Even so, many business leaders still keep their IT teams at arm’s distance, dictating the needs of the business by fiat and awaiting the results with little idea of the finer details of what their IT team is trying to accomplish.
That mindset will have to change as business leaders increasingly rely on data for making key decisions. Greater collaboration and regular communication will be necessary between business leaders and IT teams on the methods and overall data collection and analysis goals.
Therefore, it’s in every business leader’s best interests to spend more time with the IT team, learning how the data management process works and the biggest challenges. Similarly, IT team members should regularly attend more managerial meetings to better understand the organization’s overall goals and how their work relates to those goals.
“Data Culture” Will Become The Next Managerial Buzzword
A greater use of data at every business level will see the rise of a data culture that places high importance on reviewing relevant data reports before every decision. We’ve seen this for ourselves at Mactores, where many of our executive-level clients have gotten into the habit of spending up to 30 minutes reviewing data reports before participating in decision meetings.
These habits need to seep into every department and sector within a company, which should become more apparent as data becomes a central pillar in how a business operates.
Business leaders would do well to encourage and expand this data culture by democratizing data access for everyone within their organizations while also increasing staff training in specialized data management and analytical concepts. This way, employees will have a better understanding of how they can use data not only to help the company grow, but also to make their day-to-day work routines easier and more efficient.
More Companies Will Adopt Explainable, Ethical and Energy-efficient AI
According to McKinsey’s recently released Technology Trends Outlook 2022, AI topped the list for the most innovative technology today, with high expectations that it will see further interest and investment as more use cases are discovered. This lines up with similar findings from McKinsey’s 2021 State of AI survey, which found that 56 percent of respondents said their organizations had already adopted AI, up from 50 percent in the 2020 survey.
We can expect this trend of growing AI use to continue in 2023. For instance, one tool that is currently getting a lot of attention is the use of AI-powered algorithms that can ethically analyze customer data without fear of misuse. This sort of anonymous analysis can go a long way in building consumer trust.
Improved energy efficiency is another thing that AI can help with as more companies look to reduce their energy costs and embrace a more sustainable business model. As more use cases for AI are discovered, the pace of adoption is likely to increase exponentially.
Companies Will Require New Data Management And Governance Systems
As ever-larger amounts of data are collected, it will be critical to ensure that data is protected against security breaches. Currently, many organizations handle their data management and security through a role-based access control (RBAC) system, in which data access is restricted based on the role of the user. While RBAC is a highly secure system, its inflexibility presents a challenge for increased data access as it makes it more difficult to share data with the people that need it.
As such, more companies will likely look to alternative data management systems that offer more flexibility while keeping a tight security lid. Two potential alternatives to RBAC include attribute-based access control (ABAC) and policy-based access control (PBAC). Both ABAC and PBAC have an advantage over RBAC in that they allow user access based on more flexible parameters, such as the time or location at which the data is accessed. At the same time, these systems allow sensitive data to be protected against any unauthorized access.
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The Rise of Site Reliability Engineering-based DataOps
With the modern analytics stack, efficient data operations will be an absolute priority. Companies will embrace Site reliability engineering-based systems to ensure efficiency in operations. These include significant improvements in measuring:
- Data quality: Data freshness, data correctness, and data lineage will ensure that the correct data exists in the system, as the correct data is necessary to achieve the right insights.
- Distributed tracing: Building an application service map that points to a failure in the exact application to improve mean time to detect (MTTD) and mean time to recover (MTTD), which will improve uptime and performance SLA of the data platform
- Embracing failure in the design, reducing toil by consistent improvement, defining and measuring service level indicators (SLI), service level objectives (SLO), translating into service level agreements (SLA)
Businesses will need to upskill employees for new technology and find new skilled engineers, collaborators, and consultants to transform their data analytics, management, and operations.
Successful IT teams employ a system known as development operations (DevOps), a set of guiding principles for reducing silos between development teams, increasing system reliability, and maximizing application performance. However, DevOps is basically just a set of abstract principles that many companies struggle to implement, especially as their data usage becomes larger and more complicated.
To help rectify this situation, Google published a paper in 2016 titled Site Reliability Engineering (SRE), which provided easy-to-understand practical advice on how to make DevOps work. Since then, SRE has slowly worked its way into the methodology of many IT teams and is likely to increase further in usage in 2023 as more organizations look to expand their data usage.
Unlike more general approaches to DevOps, which strive for a perfect system, SRE encourages developers to accept that bugs and errors are part of the software development process and should be worked out through small but frequent updates. With automation tools, these updates can be rolled out consistently, minimizing incompatibility issues while also providing feedback loops to measure system performance. The result is an approach to DevOps that allows for greater quality control by placing system resilience at the heart of software development.
Driving the Future with Data
With each passing year, data becomes a more central component in not only the world of technology but also the world of business. Nearly all businesses today need at least some access to large data sets that they can use to root out inefficiencies, reduce costs, and design new products or services. As we move through the new year, it is imperative that enterprise companies get serious about embracing the technologies and policies of a data-driven business model.
Were these predictions helpful? How are you tailoring your data management strategies to the future? Share with us on Facebook, Twitter, and LinkedIn.
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