Learn how Infostrux data professionals can help unlock the value of your company's data.
Harnessing value from data is a skill that separates the wheat from the chaff in today's digital economy. To quote Frank Bien in his seminal book Winning with Data: "A small number of companies have restructured themselves, their hiring practices, their internal processes, and their cultures to seize the opportunity provided by data. And they are winning because of it. They exemplify the future. Inevitably, these techniques will diffuse through industry until everyone remaining employs them." Unfortunately, the diffusion of data management techniques that have accelerated growth for key enterprises remains to happen for most of the economy. Companies that have excelled in this area have continued to refine their efforts and accelerate past the competition.
With the recent popularity and efficiencies unlocked by technologies like Large Language Models (LLMs), predictive analytics, and other advances in data technology, businesses are lining up to accelerate their digital transformation. However, companies often need to pay more attention to the sheer work and planning required to lay down the foundational elements of a practical data management function. These foundational elements are crucial to leveraging advanced use cases such as LLMs and techniques such as forecasting. It does not come cheap, it does not come easy, and it won't happen as fast as anticipated. You need a well-defined Data Strategy to set your enterprise on the right track for data-driven transformation.
Here at Infostrux, we help clients accelerate their maturity in data management activities. Unlike other consulting firms, we don't provide staffing resources that execute requirements verbatim. We offer a consultative mindset where we analyze your current data management activities and understand your business goals, organizational structure, and industry landscape. We identify gaps in your people, process, and technology and create custom roadmaps to help accelerate your business up each data management core competency. Enabling you to meet your strategic business goals and objectives and harness state-of-the-art advanced use cases like Large Language Models to their fullest extent. Let's review our involvement with clients across the core data management activities.
Data Management Strategy
When clients come to us, they already have a specific use case or area of concentration; usually, a migration to Snowflake or a Machine Learning use case like sales forecasting. While we start unpacking the requirements and business context, executives often need help with an overall strategy for their data management function.
The data management program's vision, goals, and objectives are essential to align stakeholders on priorities and procure necessary support or funding. We often work with executives to guide them through establishing and communicating vision, goals, and priorities in an incremental order that builds on critical foundational areas such as structured data integration, metadata management, and data governance functions. The outcome is typically captured in a program scope document. Additionally, we make organizational suggestions to support the data management function better.
We focus on achieving incremental wins for our clients, which can be used strategically to justify new funding for building up the data platform and show actual business value.
Data Governance
A common ask, especially from clients seeking help with new warehousing initiatives, is, "How do we get started with Data Governance?". They often state it's a blind spot in their roadmap and organization. The approach here is to understand their informal data governance practices and do a lightweight assessment to understand their maturity level. We then make recommendations as to areas they need to tackle, and given that the majority of clients' point of departure is the creation or ongoing development of a data analytics function, we focus on the Data Integration process.
The work vertical can be broken into three categories. Governance Management is a principal category. Services can come in the form of coaching on Data Stewardship responsibilities, understanding the Data Product lifecycle, making recommendations where Data Stewardship and other Data Governance roles need to come into play, and then suggestions around forming a Data Governance Council if the client is ready.
Another central area of concentration for us is helping clients with a metadata management strategy; this will usually involve helping assess and procure a modern data catalog and help with implementation. Through this approach, we will address metadata management areas such as business glossary, data dictionary, some aspects of lineage, and central governance workflows related to metadata. All these metadata components are essential in ensuring your data assets are governable and discoverable by all business areas. Having metadata management in place is a foundational requirement for adequate data quality.
Data Quality
Regarding Data Quality, we take a holistic approach to helping clients. At the technical level, we have a standard suite of tools around Snowflake that monitors data quality from raw data loaded into the warehouse and implements data quality tests along the data pipeline to ensure the data transformation is deterministic and of high quality. We also leverage Data profiling and cleansing in the technical implementation where appropriate. Our technical team utilizes our IP for this or will help the client implement modern data quality management software like that offered by companies like Collibra or Monte Carlo. Before getting to technical implementations, we help clients define a Data Quality Strategy encompassing technology, processes, and people.
Data Quality Strategy will vary from client to client, but some common elements exist. Firstly we work with the client to understand their most important data products, then know within those assets what specific data elements need to be monitored for what data quality dimensions. We then set up the proper technical tooling and help the client implement the right data quality stewardship roles and processes for ongoing data quality assurance.
Suppose the client has an existing Data Quality issue impacting business intelligence or an operational system. In that case, we will do a full investigation, including recommendations on people, processes, and technology. While we leave it to the client to fix source system issues, we implement the necessary monitoring and technical controls at the data warehouse level to ensure the quality is continuously monitored. Additionally, we help the client put the correct data quality stewardship roles in place to manage the ongoing health of the data asset.
Platform and Architecture
At Infostrux, we believe that Snowflake Cloud Data Warehouse is critical to any modern data strategy. We have built our practice around it for a good reason: it provides the most cost-efficient, low-maintenance, and comprehensive data management functionality on the market. Snowflake and an ever-growing field of technically integrated third parties addressing data integration, governance, life-cycle management, and other vital areas are the core building blocks of our technical strategy. We see Snowflake Data Cloud as a trustworthy platform that goes far beyond traditional data lake/data warehouse use cases and enables data sharing, data monetization, data science, AI/ML, and, with the addition of Snowpark Containers, just about any other workload.
We help clients with greenfield migrations and improve data warehousing initiatives around Snowflake. We take a DataOps approach to technical implementations, bringing software engineering practices into the world of data development. We design the client data platform to be optimized to enable the acquisition, production, storage, and delivery of data to meet the business and technical objectives of the organization. With existing data teams, we take a collaborative approach, improving efficiency and quality, accelerating development cycles, and improving the team's maturity.
Data Operations
Having a well-defined, properly governed data product lifecycle is an area where companies need help. Often this is the sort of thing that takes time to develop. Still, companies that take the time to ensure that their organization understands, maps, inventories, and controls data flow through different business processes and lifecycles reap significant rewards.
This discipline directly correlates to improved return on investment, better decision-making, and enhanced risk management. Additionally, companies that master this space can leverage techniques like external data sourcing efficiently and securely. External data sourcing can fuel cross-selling, sales forecasting, and other advanced business use cases.
At Infostrux, we can identify gaps in your current Data Operations and create process improvements to address these gaps. We will sit with you and help design your data product lifecycle for various use cases. We have the expertise to help create policies, processes, and governance and develop the technical architecture to support data flow across your organization.
Conclusion
In summary, harnessing the full potential of your company's data is a task that requires vision, management, planning, and execution. Understanding and developing the foundational components of a data management strategy is critical to leveraging advanced analytics techniques such as sales forecasting, cross-selling, and a complete view of your customers.
At Infostrux, we can help you with the planning, management, and execution of your data management program. Our professionals are skilled in data management, data governance, data integration, data product, data architecture, data modeling, machine learning, and various other data-related disciplines. We come from different business sectors, such as retail, healthcare, government, fintech, and biotech. Our delivery teams knit together our skills and help your company comprehensively with data management solutions that map to your unique business objectives.
Contact us and let Infostrux become a core part of your data strategy.
Thanks for reading my blog post. I'm Shravan Deolalikar, Principal Data Architect for Infostrux Solutions. You can follow me on LinkedIn.
