With several data sources, it is no surprise that this client faced long-term challenges when it came to processing and securing data. Along with this challenge came a few more we’d like to highlight today. 

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. 

Reducing Data Loss

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

Inability to Create a 360 View

Building a 360 view is both labor and time intensive due to the distribution of data across systems. 

Data Analysis Flexibility

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

Inability to Enable Data Science

A lot of effort goes into getting data ready for AI and ML. 

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. 

The Need for a Platform That Can Evolve with Them

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

Increasing Accessibility Through Data Centralization

The data platform represents a centralized environment to collect all the data from operational systems across the organization. Regardless of its origin in the organization, scientists wishing to access data for analytical purposes have a single destination.

Reducing Data Loss

As the data is centralized within the data platform and maintained over time, current and historical data for all processes within the organization is preserved. This would directly mitigate the risks of data loss. 

Creating a 360º Viewer

Since the data is gathered, collected, and logically integrated on the data platform, producing a 360-degree view of the data can be automated.

Data Analysis Flexibility

All of the data maintained on the data platform, across various levels of refinement, are available for consumption through standard data communication protocols supported by the majority of data analysis tools on the market. If a specialized format is required, custom pipelines can be built. This will allow scientists and researchers maximum flexibility in analyzing the data with whatever tools they see fit.

Enable Data Science

Pipelines can be built within the data platform to prepare datasets for AI and ML workloads automatically. The datasets can be built and accessed from any level of data refinement. Versions of these datasets will also be maintained for later access by data scientists.

Data Source Agnosticism

We can accept any data source because the data resides in an independent logical model. The data can be extracted and transformed to comply with any model. This provides the freedom to transition from one particular system to another or even add systems for the same process.

Evolvability

• The data platform is an ecosystem of analytical systems with a storage engine. The composition of the ecosystem can be adjusted to accommodate growth and future needs.
• Storage can be expanded to accommodate larger data volumes.
• Analytical systems can be multiplied to handle increases in analytical workloads.
• New analytical systems can be added to handle specialized needs or increased analysis frequencies.
• Because the data is maintained in all stages of refinement (including raw data), the architecture governing the interactions between the various components in the analytical ecosystems can evolve to adopt industry best practices as they arise and mature.

With data strategy, these issues can be confronted and gaps can be bridged in order to make this client's data sourcing, storing, and sharing system more effective and efficient. Not only will this provide relief for many data frustrations but setting up a data strategy now will save time in the long run.