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Centralizing data: the key to fast convenient analytic insights.

Updated: Feb 14

Business leaders are clamoring for more insights into their business operations, customer demographics and industry trends. As the amount of data continues to grow and emanates from numerous sources, the need for centralizing data becomes more critical than ever for harnessing business analytic insights.


Centralizing data is the third step in the Business Data and Analytics Journey.

Centralizing data with Scalesology image

What does it mean to Centralize Data?


Centralizing data is aggregating and storing data in a secure way so that the data can be used for analysis. Typically, this involves moving the processed and sometimes unprocessed data into a database, data warehouse, data lake or a data lakehouse. Choosing the right repository tool for your data is based on key factors such as the type of data, the type of analysis that will be conducted, and how many users will be consuming the data for analysis. Below are definitions of tools used to centralize the data:


  • Database – A systematic way of collecting data so that the data can be accessed, analyzed, transformed, and stored in such a way to be queried for efficiency.

  • Data Warehouse – A system designed to store and optimize the querying of structured data used for reporting and data analytics.

  • Data Lake – A system designed as a central repository for all types of data both structured and unstructured.

  • Data Lakehouse – A system designed to be a combination of both a data warehouse and a data lake.


As you can see above, key factors of the data such as what insights you wish to gather and the individuals using the data, steer businesses in identifying the best repository tool for centralizing data. For instance, if you have structured data (data containing rows and columns) and you are building dashboard reports that start with Key Performance Indicators (KPI’s) and drill down to detailed information about the KPI for say thirty users, then a data warehouse is your best option. Because the data warehouse is best for structured data and is designed for querying data quickly, which is great for Business Intelligence (BI) drillable dashboards.

On the contrary, if you have large amounts of data comprised of emails, social media posts, and images (unstructured data), a data lake would be the best solution. Data Lakes are great repository tools to store everything without the need to process or cleanse the data. Data scientists can then grab, process, and cleanse subsets of the data that need to be used for analysis.


Why Centralize data?


Centralizing data allows an organization to consolidate all their data into one place making the process of managing, accessing, and preparing the data for analysis much easier. Specifically, below are bullet points highlighting the practicality of businesses centralizing data:


  • Faster up-to-date information: Centralizing data simplifies the process of querying and analyzing data. The data repository can tie directly into dashboards allowing for an easy flow of real-time information.

  • Single Source of Information: Data Analyst/Scientist have one location where they can locate information relevant to the analysis they are conducting. Having a single location reduces the risk of inconsistencies and discrepancies that arise when data is scattered across different data sources.

  • Simplified Integrations with other data sources: Having the data centralized along with the right data repository tool allows data pipelines to be built to automate the transfer of the data for multiple applications and data sources.

  • Data Governance: Centralizing data aids in compliance and regulatory activities. Businesses can effectively track and monitor user and data access, changes, and usage patterns, ensuring they are adhering to legal and corporate regulations.

  • Enhanced Data Security: Centralizing data enables better control over access to data. Businesses can implement robust security protocols, encryption, and user access controls to protect sensitive information, thus reducing the risk of data breaches.

  • Improved Data Quality: Centralizing data aids the process of validation, cleaning and standardizing the data.

  • Scalable Solution: Centralizing data allows for the seamless expansion of data storage and processing capabilities to meet increasing data and analytic demands. Data migration efforts whether to a new data repository tool or bringing in data into the current tool is more manageable and less risk of data loss.


Conclusion:


Bottomline, centralizing your data enhances your ability to get analytics quickly and efficiently; It is important step on the Business Data and Analytics Journey. The added value of improved data quality, enhanced security, streamlined processes and faster reporting and analytics, makes centralizing data well worth the effort. Ready to begin this journey for your organization? Contact us at Scalesology and let’s together ensure your business scales with the right data insights and technology.





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