Simplify Your Cloud Data Journey

Building or advancing a cloud data strategy is challenging work that can at times end up in a multi-year frustration. The technology available can feel like the wild west as considerations range from structured data warehouses to all-encompassing spark-based platforms. Unfortunately, the strategies can vary as much or more than the technology; a fact further intensified by the intense sales messaging from the cloud providers. Knowing where to start, what tools to investigate, and what capabilities can ultimately be unlocked are enough to make even the boldest technical and business executives a bit uneasy.

In this edition of Healthcare's Data Innovations blog I’m sharing our team’s three step approach to accelerating our client’s cloud data journeys. The road is still long, but by utilizing this simple approach significant clarity is added and the uncertainty becomes manageable, which ultimately contributes to creating a competitive advantage.

Before jumping to the plan, I want to take a moment to acknowledge the fact that cloud data strategies can come in different sizes. In legacy institutions an enterprise data strategy requires long-term CXO engagement and potentially years of planning. This plan serves those institutions and holistic enterprise data strategy creation and implementation; however, it also is well fit to business vertical and department leaders that are charged with improving their data capabilities in the short and near term.

So, let’s hop in. The three steps of accelerating your cloud data journey and creating competitive advantages are:

• Unite Your Data
• Govern & Secure Your Data
• Infuse Your Data with Intelligence

Treating each of these steps as separate “containers” on your journey will create the certainty you need, not only in evaluating how to take the next steps, but accepting iterative agile growth in each as your journey matures. These three areas of focus will allow leaders to communicate in clear terms with their team, users, peers, and leadership. Maturity curves can be wonderful, but in our experience, it is much easier to discuss your journey internally and externally (and garner support) by more effectively communicating progress in each of these core areas while also creating certainty in the path forward.

Unite Data

This step is about recognizing the importance in combining your most relevant and critical data assets into a centralized data storage solution. In our experience, we typically advise a commercially available cloud data platform, like Databricks, Snowflake, Microsoft Synapse, AWS Athena, or Google Big Query. Uniting your data assets creates a critical foundation to do the following:

1) Automating the manual work your team and organization are doing already (typically in isolation or piecemeal fashion) to gain a more holistic understanding. You want your team focused on delivering value – not manually munging and moving data while decreasing your security posture.

2) The same organizational data assets have exponentially more value when combined than they have in their existing silos. It’s critical to answer challenging questions in near real time – the toleration for days or weeks to generate an answer due to data challenges feels limited in our current environment. Uniting your data in a centralized solution puts you on the right path to add value in all post-processing efforts, from reporting to general pre-trained transformers.

3) Enhance the quality of your data assets through centralized stewardship and governance. Improving cross functional cooperation to unlock the value that lies within your data is imperative. We encourage our client’s to consider platforms that will enable them to inject their business logic (transformations) directly into the platform which ultimately enables the shift from ETL (extract, transform, and load) to ELT (extract, load, & transform). The ability to model your data in raw, cleansed, and curated layers is imperative to your maturation efforts.

Govern & Secure Data

Sharing data can be tricky. Each of us have worked with peers and leaders who want to hold their data close and believe that silos exist for a reason. That reason usually presents itself to be their own belief that noo ne could understand or ever manage their data better.

Sorry for any traumatic memories I may have just conjured back up in our readers! But trust me when I say that even these individuals can be won over with this process. The truth of the matter is that you have an opportunity to ease the governance and stewardship burden experienced across the enterprise, your vertical, or your department.

Initial concerns diminish quickly when you prove the ability to create an environment for safe data sharing both internally and externally. Additionally, it’s important to address how your efforts will bring the data analysts (and their tools) closer to the data by enabling them as users within a mature platform. Again, in our experience, we see our clients focused on cluster-based solutions which provide an independent data management feel for end users while providing the necessary architecture to share data and do team science analysis at scale. It’s safe to pick a popular platform that also enables data sharing through a user interface – this is where Databricks tends to shine as Unity Catalogue makes data access, provenance, and lineage manageable.

Infuse Your Data

Data is just that, data. In and of itself, data is not valuable – however, the interpretations, rather it be by humans or machines that data enables represent its true value. Designing your strategy around infusing your data with intelligence is incredibly important and requires an honest look at your organization, vertical, or departments current capabilities and the problems you want to solve. #AI has its place, but it’s rarely the simplest solution to a core business problem, it’s critically important to consider your team’s current skillset and the business’s current demands.

Many of our clients start the “infuse” process by bringing data analysts and the tools those analysts use closer to the data. Optimize your reporting efforts by offloading the calculations and manual modeling. Your team is likely working hard to create and maintain heavy logic within your reporting tools, integrating that work into automated workflows within your platform not only optimizes speed and reliability of your reporting but it allows your team to operate at their highest level by interpreting data, not managing it.

Increase your security posture as well in “infuse” by maturing your layered / medallion data structure as your team’s skills improve. Making this layered data available via secure APIs that are easily governed allows data scientists to accelerate their work and reporting analysts (and end users) to trust what they see. We’ve all been to executive level meetings where four different answers exist to the same question and that’s not a great feeling.

Additionally, it’s important to consider how to add fuel to your analysis efforts via team science enablement. Products like Databricks and Snowflake have mature analytics environments readily available for your data scientists via workspaces. These workspaces allow users to share their queries, notebooks, and generally work together to solve their most challenging questions. Bringing your team into your platform environment adds significant automation when compared to individual Jupyter notebooks and they’ll value UI functionality to call definitive statistics, train models, and even create ensemble computer vision models through CNNs.

Lastly, our team supports our clients by helping them identify what capabilities should be prioritized when it comes to “infuse” efforts. There are three ways that value can be added through infusing your data with intelligence:

1) Visualizations help users understand their data and provide just enough self-discovery options that end users can answer challenging questions, but templatized enough to maintain centrally.

2) Statistical analysis is the next step and allows users to uncover how data can help the team, department, and organization make more informed decisions and feel confident about what the data is telling them.

3) ML / DL transcends analysis and transforms your business operations through automation and enhancement – how can your data augment your current business capabilities? The LLM hype can be turned into use case certainty with the right strategy and implementation of Unite, Govern & Secure, and Infuse.

Conclusion:

This three-step approach of Uniting, Securing & Governing, and Infusing Data with Intelligence paves the way for organizations to effectively manage and utilize their data assets. Embracing this methodology not only streamlines data management processes but also unlocks new avenues for enhanced operational excellence. By centralizing data storage, ensuring robust data governance and security, and integrating advanced analytics and machine learning, teams can elevate their services to new heights. As we navigate the complexities of data management, this structured approach offers a clear and pragmatic pathway to harnessing the full potential of cloud technology, ultimately leading to a future where data-driven insights foster breakthroughs,