But the biggest challenge facing organizations today is the volume of data on disparate systems, making it hard to see the wood for the trees. Throw into the mix the sudden shift to remote working during the pandemic, and companies are dealing with data that’s scattered, isolated, and therefore meaningless.
Bringing together these disparate data sources is key to understanding markets and creating better customer experiences. From finding ways to bridge the gap to harnessing AI management and integration, it’s vital to act now in order to develop the high-value business intelligence of the future.
Leaving legacy systems behind
As we deviate from legacy systems and move to the cloud, IT managers are quickly realizing how much data is out there. The problem they’re facing is the way it’s been stored in disjointed places with no easy way to standardize the format.
The impact of COVID-19 has left IT and networking teams scrambling to adopt cloud solutions in order to support a distributed workforce, and this has meant leaving on-prem systems sooner than expected. We’re now catapulted into an age where leaving legacy systems behind isn’t a luxury decision, but an essential part of business transformation.
Even in the post-COVID world, remote working is likely to continue, and as organizations shift to a mix of dynamic private cloud and public cloud environments, migrating and managing data in the right way will be key.
How disparate data is holding you back
Data silos happen for many reasons, including outdated workplace culture, where departments don’t collaborate effectively. Sometimes, this can be due to competition, and other times simply out of habit. Add in the transition from on-premise to cloud solutions in staggered migrations, and you’ve got a tsunami of useful data stored in useless, hard to reach places.
The main issue with legacy systems is that they’re siloed, inefficient and uneconomical. But even in the early days of cloud migration, the problems were the same. Data was still managed in silos, and was therefore still inefficient when it came to intelligence.
As digital transformation takes hold, businesses must find ways to connect the dots. Otherwise, they risk always being stuck in the information-gathering phase, never reaching constructive analysis. And the sheer volume of information can be enough to keep your teams busy, without ever being productive.
To overcome this vicious cycle, IT leaders must create a business case for technology investment. Flexible budgeting, as opposed to static budgeting, can be beneficial too as it allows you to create one to two year plans, instead of sticking to the rigid five to ten year strategies that quickly get outdated. With advancements developing faster than we can keep up with, flexibility is everything. And businesses that stay agile will be able to respond to trends, changes and volatile markets more effectively.
Harnessing data with a growing number of endpoints
More than half of internet users now access the web via mobile, and the choice of devices continues to grow. From virtual assistants like Alexa to wearables such as IoT watches and health trackers, data is now everywhere.
The same thing is happening with employee data within organizations. With the rise of the remote workforce comes an increase in endpoints and devices. While some IT departments have strict allocation and setup of company devices, others have a BYOD setup. And further complicating matters are endpoint operational systems that have extended functionalities to export data from across your organization, creating even more data pools.
In order to bring all of this together, a data management strategy is crucial, and integration tools should be a priority. But choosing a scalable platform is the most important step. As the business grows, you’ll want to make sure the technology in place will be able to help you ramp up quickly, and you’ll want to be able to get value from your data without limits.
Building the right data foundation is necessary if you want to tap into greater insights for your organization, and features such as machine learning and smart data discovery should be at the top of your list.
Using AI solutions to tame your data
Not only can AI management solutions accelerate data analysis and give you fast insights, but they provide visibility of where and how your data is collected, something that many organizations lack.
IBM has one of the leading data science platforms, enabling you to have better visibility, easily collaborate across teams, and scale up at speed. Through IBM’s AI-powered tools, you can get unified data analytics, bringing your disparate data together and helping you make smarter business decisions.
What happens when disparate data becomes too hard to manage is that data sets get overlooked. But missing out on data is just as costly as bad data. Ultimately, it leads to gaps in knowledge and can skew any analytics that you already have in place. Additionally, it can create ineffective use of resource, often leading to multiple teams or departments doubling up on the same work.
If you want to boost productivity, improve decision-making and make your customer experience seamless, it’s time to invest in machine learning and automation. IBM tools help you gain a deeper understanding of your data with easy-to-use analytics, and will put all your data to work, as well as giving you better data governance.