How to plan your data strategy for an uncertain future?
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HOW TO PLAN YOUR DATA STRATEGY FOR AN UNCERTAIN FUTURE ?

By 21 December 2022 No Comments

The new year is coming, and so are new trends and issues regarding data. We gave you the trends for 2023 a few weeks ago. Now let’s look at the issues that companies will need to address, according to Database Trends and Applications. 

 

How to adapt your data strategy for the future 

  • Data as protection against economic turbulence: 

Economists predict more economic instability in the future. To face that, companies will need to harness and use a maximum amount of data to survive and secure growth in an increasingly competitive market. 

Strategic decisions regarding data must thus be placed at the heart of the organization. 

 

  • New threats on data security: 

With data and attack vectors growing, cybersecurity is becoming more and more complex. In 2022 we discovered new types of attack, like attacks on multifactor authentication systems. 

Companies must learn to protect themselves against such attacks. That includes using local passwords on backup servers, using multifactor authentication when possible, storing backups in servers inaccessible to users, using a non-Windows backup server, copying some of the backups on an immutable cloud. 

 

  • Smarter scaling for better growth: 

According to the CEO of Ascend.io, Sean Knapp, one of the challenges regarding data is centered around scaling the production of data products and increasing team productivity. 

By choosing the right foundational platforms, organizations can focus on business value and conducting change, instead of managing technical complexity and compromising on data and scale. 

With data automation technologies, companies can greatly reduce the number of tools and integrations in their system and simplify its management, which makes finding talents easier. 

 

  • More automation and low-code: 

The volume of data growth and the complexities of multi-jurisdictional usage are a new challenge that companies must face. According to Jim Sears, vice-president of professional services at Boomi, automation and low-code offer an appropriate response to these issues. Indeed, those 2 elements allow for the automation of data streams and make their update easier. Plus, the addition of AI makes the process smart.  

 

  • Breaking the silos: 

Data silos have existed since the first database was created and are now a major problem. 

New data connectivity solutions give companies the opportunity to build bridges between their SaaS applications, their directories and their platforms. 

According to Karanjot Jaswal, co-founder and CTO of Cinchy, data should be separated from the applications that create and store it and be brought to a unified network. 

 

  • Data extension:

Data sprawl and governance are becoming more complex to handle as data is distributed across the data center, and edge, hybrid or public cloud infrastructure. 

Companies are looking for a hybrid cloud operating model that combines the agility and automation of the cloud with the ability to easily connect their applications, data and infrastructures across on-prem, near-cloud, and public clouds. 

Multicloud, on-premises and edge environments are going to become more common to keep up with the evolution of the demands.  

 

  • Responsive infrastructures: 

According to Jeremy Bentley, head of strategy at MarkLogic, data management approaches centered on application and integration are reaching their limit, and the next step is the “data-first” approach. Those who have understood that are thus taking interest in concepts like data mesh and data fabric.  

 

  • Alignment with customer experience: 

Modernization is key. Indeed, according to Steve Zisk, senior product marketing manager at Redpoint Global, companies that have significantly improved their digital and hybrid customer experience have experienced a much bigger growth than companies that haven’t changed anything.  

 

  • Talent shortage: 

Today, there are more companies looking for data experts than data experts available. While the market balances itself, companies can train their staff and make their culture and their data strategy evolve to attract and retain talents.

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