Gartner, Inc.’s latest forecast reveals that global IT spending is projected to reach $4.6 trillion in 2023. Despite the impacts of COVID-19 and the looming recession, enterprises will push forward with digital business initiatives and invest in technological advancements.
“Enterprises will prioritize technology investments this year [2023], said Ammar Ali, Business Analyst at Astera Software. “Amid economic instability and uncertainty, businesses would require actionable insights to identify and meet specific business drivers. Defying conventional wisdom of cutting IT budgets, forward-thinking CIOs will pursue efficiency-driven digital investments to ensure profitability and market competitiveness.”
In today’s digitized environment, data is a valuable asset for enterprises. It can be used to maximize efficiency, drive business growth, and gain a competitive advantage. Businesses without a clear data strategy risk missing out on great opportunities and falling behind their competitors.
Defining your data strategy
Failing to plan your 2023 data strategy at the start of the year is planning to fail. Get on top of your data management at the start of the year, focusing on creating a data ecosystem that delivers value right from the start. It’s not that complicated if you follow a systematic approach.
Formulating a 2023 game plan
So, the first step is obviously defining the objectives of your 2023 data strategy. You must identify business goals that you want to achieve through data-driven insights in the coming years. The goal can be increasing revenue, improving efficiency, delivering a better customer experience, or something else.
Once the goals are identified, the next step is determining how data can be used to achieve these goals. This will involve identifying specific data sources or types of data that can provide relevant insights pertinent to business drivers that influence those goals.
Now the tricky part is creating an end-to-end data architecture to automate data ingestion, processing, and loading. Automation is crucial here as a manual approach toward enterprise data management is ineffective in terms of costs and efficiency.
Why is defining a data strategy important?
It’s a known fact that enterprises that make decisions based on data are more likely to succeed than those that rely on intuitive decision-making. Defining a clear data strategy should be your top priority in 2023 due to these three reasons:
Data privacy regulations are becoming more stringent:
Recently, there’s been a growing focus on data privacy, with new regulations, such as the General Data Protection Regulation (GDPR) coming into force. Companies without a clear data strategy risk falling foul of these regulations and facing financial penalties and legal repercussions.
Data volume and variety are increasing:
The amount of data being generated is increasing rapidly, and this trend is expected to continue in the coming years. Without a well-thought data strategy, enterprises will get overwhelmed by the sheer volume and variety of data they must store and process for reporting and analytics.
Data-driven insights are imperative for informed decision-making:
In today’s business environment, it’s important for enterprises to be able to make informed decisions based on data-driven insights. A comprehensive data strategy can help organizations to understand their data assets better and effectively utilize them to improve decision-making.
A data strategy can help you unravel opportunities for data-driven innovation to stay ahead of competitors and drive business growth.
Moving to the cloud
You might want to consider modernizing legacy infrastructure and remodeling data architecture to support your data-driven initiatives. Modern cloud technologies are usually the preferred choice of modern enterprises owing to several benefits it brings to the table, including:
Cost-effectiveness:
No costly infrastructure and maintenance costs
Scalability:
On-demand access to additional computing resources
Speed:
Rapid access to unlimited bandwidth speed as needed
Consumption:
API support to connect with third-party services
Business Continuity:
Disaster recovery, data deduplication, and backup
These are some of the many benefits of the cloud. The technology is continuously evolving with innovations that expand the horizon of cloud computing use cases.
Overall, access to virtual computing resources is highly advantageous for modern enterprises as they can be effortlessly adapted and modified to fulfill specific business objectives.
Is this a fact or suggestion?
Fact! Modern enterprises are increasingly adopting cloud-based data management solutions. RightScale’s report shows that cloud adoption among enterprise organizations is over 94 percent. The same report shows that 84 percent of companies have a multi-cloud strategy, which involves using private and public cloud-based solutions.
The cost-effectiveness, accessibility, scalability, security, and disaster recovery capabilities of cloud solutions encourage companies to move beyond traditional on-premises systems. This trend is likely to continue and even grow as enterprises continue to recognize the benefits of cloud-based data management.
Data governance
With the increasing volume and complexity of data being generated, you must have effective data governance practices in place. This involves establishing policies and procedures for managing data and ensuring that it is accurate, complete, and secure.
Modernizing data architecture can help improve data governance in several ways:
Improved data quality:
Store, manage, and access data more efficiently.
Enhanced security:
Implement advanced security measures, such as encryption and access controls.
Better data access:
Store and manage data in a way that makes it more accessible to authorized users.
Greater scalability:
Store and manage larger amounts of data without sacrificing performance.
Improved compliance:
Track and manage data access and usage to meet regulatory requirements.
In coming years, businesses are likely to place an even greater emphasis on data governance as they seek to ensure that their data is collected, stored, and used in a way that aligns with their values and goals and complies with industry regulations and privacy laws.
Data security
Of course, data security should be your top priority, and you must have security measures such as encryption, access controls, and incident response plans. As the importance of enterprise data increases, so does the need to protect it. Here are some measures you can take to protect your data:
Implement access controls to ensure that only authorized users can access the database and sensitive data
Regularly update and patch the data management platform to fix any vulnerabilities or security holes
Formulate a disaster recovery plan to guarantee that your data is backed up and can be recovered in case of an emergency
Educate your employees about data security best practices and the importance of protecting sensitive data
Conduct regular security audits to identify and address systems vulnerabilities
Should you use data lakes?
Perhaps. If you’re dealing with large volumes of unstructured and semi-structured data, data lakes can be a good option. A data lake is a centralized repository that allows you to store structured and unstructured data at any scale. Data lakes can be used to store and process data from a variety of sources, and they are becoming increasingly popular for managing and analyzing big data.
What about artificial intelligence and machine learning?
Artificial intelligence (AI) and machine learning (ML) technologies can be used to automate and optimize various aspects of data management, such as data cleansing and analysis. Creating a data architecture with self-regulating data pipelines will allow you to seamlessly process and store massive volumes of data with minimal manual intervention.
Moreover, using ML technologies like natural language processing and robotic process automation will facilitate advanced data analytics. As a result, you’ll be able to unravel actionable insights to make more informed decisions and improve the efficiency of your business.
AI and ML technologies are becoming increasingly prevalent in the business world, and they rely on large amounts of data to train and improve their performance. In the future, companies are likely to increasingly adopt AI and ML technologies, which will require effective data management systems to support them.
The code-free environment
There are several reasons why companies might choose to use code-free data management solutions:
Ease of use: Code-free solutions are designed to be user-friendly and easy to use, even for those with little or no programming experience. This makes them accessible to a wider range of users and enables them to get up and running quickly without the need for extensive training or technical expertise.
Faster deployment: Low-code and zero-code solutions doesn’t require extensive programming, which means they can be deployed more quickly than solutions that require custom code. This can be especially important if you want to get a new data management solution up and running quickly to meet pressing business needs.
Lower costs: Code-free solutions are less expensive to implement and maintain than solutions that require custom coding. They don’t require ongoing maintenance or updates by a team of developers, saving you significant money in the long run.
Overall, code-free data management solutions can provide a more cost-effective and efficient way to manage and analyze your data without requiring a large investment in specialized skills or resources.
What does the future look like?
In 2023, it is likely that businesses will continue to rely on data to drive decision-making and drive business growth. As data volumes continue to grow, the need for effective data processing and storage systems will also continue to grow. Enterprise data management technologies are constantly evolving, and their future is likely to be shaped by a number of trends and developments, including:
Increased emphasis on data governance
Greater focus on data security
The continued growth of big data
Increased adoption of AI and ML technologies
Greater integration of data across systems and processes
Overall, the future of enterprise data management is likely to be shaped by a combination of these trends and developments, as well as by changes in the business environment and the needs of organizations. Businesses will need to have effective systems in place for managing their data to stay competitive and drive growth and success.
Effective data management enables companies to gain insights from their data and make data-driven decisions that can improve operations, increase efficiency, and drive business growth. It also helps companies to better understand and serve their customers, as well as to identify new opportunities for innovation and expansion.
Enterprise data management is crucial for modern companies because it helps them extract, store, and process effectively and analyze data to make informed business decisions. It allows companies to organize and manage their data in a way that is efficient, secure, and compliant with industry regulations.
So, what’s your data strategy for 2023? Do you have a game plan to manage and utilize your business data effectively? We surely hope this will serve as a good reference point to get on top of your data-driven initiatives.
At Astera, we provide unified, end-to-end data management solutions to modern enterprises. Check out this video to learn about Astera Data Stack, our no-code, unified platform, in 59 seconds:
Are you interested to explore Astera Data Stack and see how it can help you become a data-driven organization? Click here to download a 14-day trial version of our enterprise-grade solution. If you have any questions, please reach out to us at support@astera.com