How do you align your data tools with your goals?

Data-Ocean has many ways and means to navigate. Tools give us the ability to achieve business goals. Each Goal has use case, having good understanding of it gives some flavours like available budget, tactical or strategic, complexity, data consumer needs etc. All these variables contribute in Tool selection. Integration tool, Storage, Visualisation layer, Data Governance, DevOps etc are just few to start. From past experience as an Data Architect, my suggestions would be analyse, compare & define break even before conclusion. Consider, maturity and global support for strategic use case. Though, no tool is perfect, showing flexibility to adopt new ways gives you an ability to navigate smoothly!

I think, the company chose both the vendor and tools primarily based on the vendor's high-level marketing pitch. The IT team of the business was then compelled to adopt the chosen tool. As the team began using the tool and gained experience with it, they might have come to the realization that the tool might not be the most suitable choice for the specific business requirements and data architecture. It's advisable to screen tools and vendors based on the initial assessment by the development and architecture teams of the business.

We apply following approach to align data tools and end goals using strategic approach: 1) Define Objectives - Clear understanding of end goal 2) Understand Data Needs - Type of data such as structured, semi-structured, unstructured 3) Select Appropriate Tools - Say document DB, Relational DB 4) Integration - Of complete pipeline 5) Data Governance Framework 6) Skill Development for our data engineers 7) Monitoring and Optimization - Open source observability tool 8) Feedback Loop - Back from front-end team to back-end team and vice-versa 9) Scalability - For future 10) Cost Management - As start-up we keep close eye on bills with every migration, new implementation or updates

Adopt Agile and data as a product methodologies with goal planning. This will help identifying your products and prioritizing. Then look at the team roles and skillsets. Understanding your team’s skillset is critical for tool selection as there could be a learning curve or resistance to adoption. Also, decoupling core architecture like data storage, compute, and management. Lastly, consider integration. Now that core architectures are decoupled integrations are more important than ever to take full advantage of these tools.

Get clarity on what you are trying to achieve with data as you map the business process flow with the data flow. Are you building a data pipeline for storage and reporting or are you integrating with an external system. Maybe you are migrating data to the cloud. Whatever the purpose, get a clear idea of the data elements involved, the relationship among them, where it is sourced from, what are the changes or transformation to the data and where will be be stored. If it is data exchange, like an API or MQ messenger service for example, what are the data elements involved in the request? What is the response like. What are the data elements involved. Are all these happening in real time or batch. What is the frequency, latency, volume of data

I'm not on the Procurement side, I'm a developer. But I've found that research on the front end yields far better results. Take the time to research the available tools, including freeware that might be considered to be approved for download. Also, requirements documents aren't everything; communication and coordination with users to find out the difference between what they documented and what they *really* need is critical to data integrity. Yes, it typically winds up extending the project plan/design stage; but it will typically be made up, and more, in implementation and test. And after-the-fact "oops"-style discoveries in the data will be minimized, saving project time and resources in the long run.

There are tools, but tools without the skills to use them are worthless. There are just 2 important factors: internal/external skills and, above all, their availability and ability to do the job.

Once you get clarity with the business process flow, and the data flow, evaluate your options. Look at the functionality, scalability, type of data, format of data etc as well as your need and functionality in determining a good fit. For example are you trying to achieve an ETL functionality. If so there are multiple options. Maybe your need is for reporting with analytics. if so, again there are multiple options. Evaluate the functionality and technical capabilities of the tool as well as other considerations like licensing, total cost of ownership etc. Then Based on your functionality, budget, resources, timelines and training, one can make an educated choice of using a tool for a specific business functionality of using data.

It is a good idea to align your tool with the functionality desired. For example if you are creating a data pipeline to ingest data from various sources, and which involves real time streaming as well, then it makes sense to evaluate the tools offering this capability. Fortunately all the leading cloud providers such as Google, Amazon, IBM, Microsoft etc provide this capability. Then after ingesting you may have a need to clean, curate and transform the data. Here you may want to look at a good data governance tool along with Extraction Transformation Loading capabilities. Or maybe you need to model and combine the data. Maybe reports are involved. So based on your functionality and sequence, align the tools for data processing

Are you tools able to handle the data volume required. What about the refresh capability? Do you need full refresh or incremental refresh, to your data pipelines for analytics. What will be the frequency. What if there is a processing failure. Can your tool handle that and does it offer checkpointing and restart capabilities. How will you validate the work. What about data governance. How do you correct bad data. What is the functionality offered by your tool. Does it have connectors to multiple external sources and systems. Does the tool have the capability to handle data of various formats like structured, unstructured, images, audio, etc, Does the tool allow for easy integration with other systems. What about performance.

Additional factors to consider are: 1. If you need the tools to be cloud native OR should they run on-premise also. 2. What type of pricing model do you prefer, fixed, variable, usage based etc.,. 3. Ability (financial and technical) of the vendor to stay in the market longer. 4. Availability of talent pool in the market for that tool 5. Training resources to train your internal staff on that tool 6. How is their product roadmap aligned with your future goals, both in terms of features and delivery time lines. 7. What is their partner exo system for better integration with other tools 8. How is their post deployment customer support system is built. Does it work for you. 9.What are implementation expenses and timelines are.