- June 15, 2020
A Unified Data Repository (UDR) is a centralized data storage unit that consolidates data collected from various, often siloed data sources. A UDR makes data seamlessly available organization-wide to all resources (human, machine, and application) via a single hub. The result of implementing UDR is a high-level and unified view of enterprise data, more accurate insights derived from multiple data points as opposed to limited ones, better data management, and governance, and unification of otherwise possibly disparate business segments.
The Need for Data Centralization
Most organizations today follow the process of departmental data collection and storage. Different business segments within an organization collect, parse, cleanse, and store data in storage units that are siloed from each other. This results in:
- The use of multiple data management solutions that perform the same or similar functions for each data silo. This could be hardware or software tools
- The need for more teams to cater to each data silo
- Increased costs due to duplicated use of software, hardware, and manpower
- A lack of organization-wide data visibility and data cohesion between departments
For an organization that treats business segments as individual entities due to security (as government bodies do, for example) or any other similar reason, this model works fine. However, for any organization that has data silos inadvertently created due to improper planning or because of oversight as the business grew, implementing a UDR is the answer to eliminating each of the pitfalls mentioned above.
Is Your Business Prime for a Unified Data Repository?
The decision to implement a centralized data repository stems from the businesses’ short and long-termed objectives. Do your business goals require data collaboration? Do daily operations require data collaboration? Will the quality of insights improve from increased data points? Will data management and governance be easier? Will there be a cost-benefit to data centralization?
Here are all the ways to identify if your business needs UDR:
Multiple User Touchpoints
If your business has an omnichannel presence and experiences user engagement from various sources, you could end up with data silos, or worse, data loss. Implementing UDR will consolidate data from every source and make it readily available within a single source in the data warehouse.
Personalization Is Important
For businesses like e-commerce that require content and interaction personalization, collecting massive amounts of data and inferring from every data point is important to creating hyper-personal content. A consolidated data container is the best way to achieve this.
Need for Advanced Analytics
Raw data is the source for analytics. If you’re looking to obtain enterprise-level analytics and reporting insights, consolidating data is the key. The alternative is a cumbersome process of collating multiple reports extracted from each data repository.
Benefits of a Unified Data Repository
UDR allows an organization to fully utilize the potential of large datasets. Implementing a centralized data store enables the organization to consolidate and standardize data while making it readily accessible, along with the benefits like:
- Enterprise-level view of data
- Improved and easier data and systems management
- Prevention of data duplication between business departments
- Prevention of unwanted costs like software licenses for each business department
- High-level analytics and insights derived from a larger data source
- Improved customer personalization
Consolidating data through a Unified Data Repository could be the key to successfully achieving business goals that are data-dependent. It also presents supplementary benefits like a lean data management team, better collaboration, and unified data flow between departments.
With an AI-first approach and strong expertise in AI frameworks, RecoSense is a one-stop partner for end-to-end Data Intelligence Solutions. Our industry-unique cognitive computing platform based on Natural Language Processing and Machine Learning frameworks offers Intelligent contextual interpretation of the Content & Users.
- Analytics (12)
- Artificial Intelligence (25)
- Aviation (3)
- CDP (20)
- clickstream data analysis (1)
- content intelligence (8)
- contextual insights (2)
- cross-selling (5)
- Data Engineering (10)
- Finance (29)
- Healthcare (1)
- IDP (3)
- Legal (1)
- Manufacturing (2)
- Marketing (21)
- Media (32)
- Natural Language Processing (5)
- NLP (10)
- Retail (60)
- Shopify (24)
- Sports (1)
- Technology (2)
- Travel (5)
- Uncategorized (3)
- Upselling (4)
AI for Fraud Detection
Mar 17, 2023AI and ML Use Cases in Insurance Industry
Jan 10, 2023AI in Equity Research
Nov 21, 2022
TagsAI AI personalization Analytics artificial intelligence boost sales Buyer Persona content discovery content personalization content recommendation cross-sell cross-selling customer data platform customer engagement customer experience customer journey customer journey mapping customer loyalty customer retention Customer Segmentation Data Analytics digital experience ecommerce ecommerce personalization Hyper Personalization machine learning magento extensions magento plugin marketing media omnichannel personalization ott ott trends personalisation in small eCommerce Personalization predictive analysis predictive analytics product discovery product recommendation retention sentiment analysis shopping upsell upselling user engagement user experience
Get regular updates on data science, artificial intelligence, machine[contact-form-7 id="2755" title="Comming soon"]
You might also like
Your A-Z Guide to Customer Lifetime Value and How to Make the Most From It
What is User Journey Mapping & Why is it Important for Media Websites?Artificial Intelligence   CDP   Data Engineering   Media   Retail
Humanizing Big Data: Key Approaches That Give You the Best of Both WorldsCDP   contextual insights
What Is Data Retention? How to Create a Policy That Protects Privacy?