Data Strategy: The Planning Tool That Can Solve Your Data Challenges 

When it comes to software development projects, data is everything. For industries like Biotech, Pharma, Manufacturing, and Fintech, data collecting and safety can make or break a project. Oftentimes, people become frustrated by ineffective data capture from various data sources, struggle to reduce data loss, and suffer from inflexible data analysis.

One thing to note: you’re not alone in these frustrations. Our experts have heard many stories of teams having a hard time navigating data challenges. That's why we’ve come up with this guide that demonstrates our solution to challenges like these and many more. Let’s get started. 

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Common Data Challenges

Data challenges are not industry specific. The battles against data frustrations often mimic each other across many projects and industries no matter the size or experience of the team. So, how can you combat this? What is there to be done about challenges so widely spread?

For starters, it should put you at ease that your struggles are not unique, but shared experiences. What’s good about shared experiences is the collaboration of ideas and tools that grow from them in order to provide a solution.

Let’s take a look at some of these shared data frustrations. 

The Challenges

1. Reducing data loss

Data can be lost or inaccessible due to a multitude of systems and media used to store it. 

2. Enabling data for AI/ML

A lot of effort is needed for getting data ready for AI and ML. Without an effective and efficient plan, this process can strain tech leads. 

3. Data analysis flexibility

When using analysis tools, data flexibility can be limited if data is siloed in a multitude of systems and formats. 

4. Increasing accessibility through data centralization

It’s not uncommon for data to exist in a multitude of disparate systems across an organization. This can make access difficult. 

5. Needs a platform that can evolve with them

Need to be able to evolve the analytical ecosystem to accommodate future needs as they arise. 

6. Data source agnosticism

If data is currently affined to the storage format of the managing systems, independent formats can be introduced to represent data and not be tied to any particular system. 

7. Needing Secure data longevity

Data security is a high-stakes topic spanning every industry. An even more important area of data security that many people struggle with is longevity. 

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The Solution

So, now that you’ve seen a few of the most common challenges and know that you’re not alone in these frustrations, it’s time to address the solution. Believe it or not, there is one single tool that can solve all of the abovementioned challenges, and that solution is data strategy. 

What is Data Strategy?

Data strategy is a long-term roadmap to manage a company's data. This roadmap lays out the technology, processes, people, and rules that will deem this strategy effective and efficient. Let’s take a look at some of the capabilities, components, and team members involved in the data strategy process.

Data Lakes

A data lake is a centralized storehouse that holds, processes, and secures vast amounts of structured, semi-structured, and unstructured data. 

Data Lakehouse

This data management architecture combines the flexibility, cost-efficiency, and scale of data lakes with the management and ACID transactions of data warehouses to enable business intelligence and machine learning on all data. 

Data Centralization

Data centralization makes all of a company's data information accessible through a single location. This allows for quick, easy, and secure access to all data regardless of originating systems.

Data Architects

This role is responsible for ensuring all tools and architecture(s) support the long-term needs of the company from an analytical perspective. 

Data Engineers

These engineers build pipelines that will acquire data from sources, push it to the data platform, and fine-tune it before presenting it for convenient consumption for end users.

Data Analysts

Analysts consume data from the platform and analyze it using various tools that turn out meaningful insights. 

Combining these data strategy roles and components allows for an effective and efficient solution for an abundance of data-related challenges. The combination of security measures and centralization aids in lessening the effects of data loss, increases accessibility, ensures data security longevity, increases flexibility, enables ML/BI for all data, and provides a platform for data to evolve with the company. 

Wondering what kind of team roles we have here at Integrant? Check out our team's page! 

Wrap Up

Data challenges are no stranger to any existing industry. Feelings of frustration and uncertainty are not uncommon for teams facing roadblocks like data loss, data inflexibility, and temporary data security. While it’s helpful to know that you are not alone in these challenges, it's even better to know that there is an existing solution to eliminate them altogether.

Data strategy is a plan built to last you long-term and create more accessible, effective, and efficient data. It’s not too late to take your current data strategy and adjust it to fit your long-term goals. Different organizations are going to have unique requirements for their data strategy and our experts have the experience and dedication to get the job done right. Reach out to our experts today by setting up your free consultation.  

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