Why Data Modernization Should Top Your New Years Resolutions List
As we usher in the New Year, it's time to set goals and make resolutions that will propel us forward. One critical resolution to consider is data modernization. Data modernization involves upgrading your data infrastructure to improve efficiency, enhance security, and enable advanced analytics. By committing to data modernization, you can ensure your organization remains competitive, agile, and ready to harness the power of data-driven insights. Embrace this opportunity to transform your data strategy and set yourself up for success in the coming year.
What Is Your Data Resolution for the New Year? Here Is Why You Need One
Data is more important than ever for your healthcare business, but you already know this. So, what are you doing to support this fact? Many companies are well into their data plans, while many have not yet begun to modernize the right foundational technology tools to keep up with their data management needs and future capabilities, specifically for BI and AI. We've never seen such monumental technology impacts on data as we have in the last four decades, even more in the last few years, and it's not slowing down. The thing is this new tech requires a data reorganization and modernization plan.
Data is part of our everyday business lives, and most of us have been on the data train for our entire careers. In 40 years, we've skyrocketed through to the internet, mobility, and cloud migration age, and now, artificial intelligence (AI).
In addition to these new technology advancements, we are seeing even shorter user adoption in each era of data innovation. While the adoption of computers took 25 years, the world adopted smartphones in just four years. This rapid pace is true, in general, for AI, too. Business to consumer products are adopting AI at a staggering rate. Now businesses are adopting AI and applying it where it directly influences and simplifies processes. With AI that can evaluate data and make human decisions on redundant tasks, freeing staff up for more important work.
Data Modernization - AI Adoption
Why Companies Struggle With Data Modernization
In my travels, I've talked with many mid-sized operators about data. In each conversation, they all realize the value data delivers, but most have not kept in step with the new evolution that data collection, storage, and the transformation tools that ultimately turn data into usable and relevant information. It has revealed that they are not aware of the new and affordable data solutions like Microsoft Azure or Amazon Web Services (AWS) and the products that link to them to augment solutions. Diving even deeper into the conversation, I find many of those same operators are swimming in tech-debt but don't know where to start to trim and consolidate their tech services.
This, coupled with the fact that most of these organizations have a small IT department that manages their current platforms and third-party products, most have not been charged with a short-term and/or long-term data strategy. This gap is usually a result of the lack of understanding from the C-suite management of the need to initiate, drive, or support a strategic data plan.
The Role of AI in Your Data Strategy
With the arrival of AI, Healthcare businesses need an internal strategy for data management now more than ever. You also need to be own it. This involves demystifying AI in data management. It's simpler than it seems thanks to that evolution we talked about earlier. Today, you can build AI solutions with the same ease as an Excel spreadsheet or BI dashboard. Soon AI platforms will build their own process solutions by simply typing a request. Why not? AI tools are already building code.
Here are a few things to consider for building your plan:
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- Understand your data technology foundational needs for your organization.
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- Identify the early AI impact points and weigh the benefit to each AI application you wish to implement and pick a single use case to start.
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- Engage in a data modernization project to establish a solid foundation to connect and control each AI solution (again: simpler than it seems).
Third-party function-focused machine learning solutions might be the best placeholders to distill data into information. Without a strategic plan, IT solutions quickly become cluttered and conflict with internal and external data platform(s). Each point where data isn't controlled, ownership and access become murky, creating a costly endeavor as one-task solutions multiply and leave an organization mired in tech-debt.
How to Start on Your Strategic Data Modernization Plan
First, do not sit idle. Companies can avoid this quagmire through vision and a data strategic plan. The foundation of a solid strategy is understanding the starting point in a company's data journey. Determine where the company is headed, focus on the greatest impact first, then build a timeline around it.
Senior strategic managers should ask themselves and their staff these questions to determine where the company is - and where it needs to be to capitalize on technology that is here today.
Q: Where is our data stored today?
The logical start to a strategic data plan is knowing if data is stored on premises or in cloud storage.
Q: Why do you still store data on site?
Why aren't we using a cloud-based solution? Current security elements, ease of use, efficient data management, and affordable storage are all key benefits to a cloud-based solution. At some point, this will become a priority as more on-prem storage software is sunsetting in the next few years. Updates are already less frequent, putting companies at risk. Transitions to cloud storage are easy to accomplish. A cost analysis will earmark a value for budgeting purposes and to validate that the move to the cloud makes financial sense with a reduction in data storage operating costs. Think: ROI.
Q: How do you store the data?
This evaluation should consider the platforms being used to store the data. What limitations does it have, if any, against more modern platforms?
Q: How is the data structured in storage?
Determine any limitations to complement the efficiencies of more modern data manipulation/processing platforms.
Q: What do you use to extract the data into useful information?
Is this an in-house tool, or does a third party currently provide this service? Finally, what information are you extracting?
Q: What are the strengths of these offerings, and what are the shortcomings?
Like any SWOT analysis, this can be a complex process that requires a deep dive into a laundry list of assets and deliverables, along with careful consideration of how they're informing -- and enabling -- success and growth. Almost every health organization has a go-to list of customized spreadsheets and dashboards to track key performance indicators (KPI) and measurement.
In today's advanced data environment, most data manipulation tools are provided through task-specific third-party providers; for many, they're easy to replicate in-house and can be enhanced with clean data and the right data structure. When you advance your own initiative, you will want to emphasize that evaluation to enhance the "likes" and eliminate the limitations in your own build.
Next Steps: How to Get Up to Speed With Data Modernization
Artificial intelligence has transformed healthcare on the clinical side, but AI also provides significant opportunity to improve operations. While many healthcare organizations have embraced AI, many midmarket-sized companies have just scratched the surface using machine learning products, the on-ramp to AI.
Consider an evaluation through a trusted data consulting organization if you are not up to speed on the latest product and industry advancements. Some organizations offer complimentary high-level evaluations covering the points listed above to help you get a plan and work it. If you don't currently have a trusted data advisor, OmniData can help. We offer complimentary assessments to evaluate your current data infrastructure and provide a roadmap for next steps.
Wherever you are your data maturity journey, we have options to help modernize your data estate, whether it's data, analytics or AI.
Start with our Analytics in Action or AI in Action workshops offers.

Healthcare Lead, Data & AI Solutions
Ron Tamol is a results-driven healthcare data specialist at OmniData, with over 20 years of experience in the healthcare marketplace. His extensive background includes roles in large corporations and two successful SaaS start-ups. Ron excels in advising C-Suite and senior stakeholders on their data modernization journeys, focusing on data storage, structure, and the application of world-class BI and AI solutions. His expertise helps optimize and advance operational and clinical processes, improve customer outcomes, and enhance profitability.