Achieve business outcomes with good data
Zscore’s Smart Data Platform seeks to enhance data quality and empower business users. Resulting in better business decision making and insights regardless of the volume of data, format, or systems.
Businesses are being transformed by
they way they use data. As
organizations invest more into newer
initiatives to innovate with data they
struggle to make them pay off. One of
the primary obstacles is that of usable
data
Data quality hampers an organizations ability to make good decisions, reduce growth and innovate. Relevant, timely and trustworthy data is key to success.
With Zscore’s Smart Data Platform, you can:
- Enrich data automatically at scale using AI
- Map business and data workflows to drive data usability enterprise wide
- Fix data quality issues using ML based automation
- Export and share data with applications to drive quality insights and decisions
An important analogy about data quality. Like the “complaint department” days of customer service, many organizations still view data quality as little more than catching and fixing bad contact data. In reality, our experience working with enterprise customers has taught that data quality plays a very strategic role in areas like cost control, marketing reach, decision making and brand reputation in the marketplace.
Data Quality in Reporting
Data quality is essential for one main reason: To give customers the best experience when you make decisions using accurate data.
Collecting trustworthy data and updating existing records gives you a better understanding of the customers. It also lets companies keep in contact with them using verified email.
Addresses, mailing information, and phone numbers. This information helps market effectively and use resources efficiently.
Business analytics of customer data is one area where the need for clean data cannot be overemphasised. Good data has a direct impact on Net Customer Rating (NCR) and Net Promoter Score (NPS) scores.
Data Governance
Data quality and data governance are both indispensable for organisations that want to become data-driven. Both may be separate practices, but they are fundamentally related.
To put it simply, you can’t have data quality without good data governance. However, data governance has evolved in the last decade and is now a hybrid of automation and manual strategies.
Solution
Zscore’s data quality platform can be in the cloud or on-premise or delivered as a managed service. An engagement is kicked off in a couple of weeks of system and data analysis. This was followed up by a two week implementation of all workflows and data recipes.
Data quality transformation is driven with the help of machine learning algorithms. Business workflows and rules are documented in the platform to help identify data patterns and weed out dirty data.
Data is cleaned automatically and issues are flagged for manual intervention, and creation of new transformations were quarantined.
Zscore’s data health dashboard indicates their DQI (Data Quality Index) and helps monitor progress.
Move From Data Collection to Data Interpretation
Customer Complaints can be reduced significantly with good quality usable data
Identify a clear linkage between business processes, key performance indicators (KPIs) and data assets
Identify corrective actions to be taken and provides valuable insights that can be presented to the business to drive ideation on improvement plans
Data assets are acquired from external sources where the DQ rules, authorship and levels of governance are often unknown. Hence, a “trust model” works better than a “truth model.”
Automate Data Quality and increase trust with customers by providing them trustable data with personalisation at near real-time
4 KEY
Data Discovery & Patterns
understand customer data, patterns & relationships
Data Quality Transformation
apply consistent data quality across departments
Error Handling
Automate data quality within the company
Data Recipes
our central repository for all data checks across your business
Key benefits
Improving Reporting Standards
Businesses are hooked onto data right now. Monetising data assets, intelligence, AI and Big Data are key for them to stay innovative. But how do you ensure the dollars being spent on these initiatives are built on a sustainable data practice and good quality data?
The tools we provide let you establish a strong foundation for your data strategy which will allow you to scale your initiatives. Our platform allows organisations to manage their data and ensure the business stands on a strong foundation of good quality usable data to become truly data-driven.
- Business analysts get their own automated tools so they can easily manage data quality tasks without dependencies on IT
- Data teams can now focus on higher value tasks than manually cleaning data problems
- Create a single source of truth to work on trustable data with greater ease of maintenance
Key features in the solution
Data Discovery & Definition
Discover critical patterns and trends in your data. Identify unique identities and the frequencies and consistency with which values appear
Business Workflows
Manage data quality by translating business workflows into data flows. Manage data quality in relation to business needs and not outside it.
Data Quality Index
Understand the impact of your actions on data quality. Set benchmarks and monitor progress towards your goals
Data Quality Transformations
Perform data quality transformations by using an extensive set of predefined algorithms, or write your own in Python, R or SQL.
Collaboration based ML
Data quality automation within the company using collaboration driven Supervised Machine Learning to fix data errors automatically.
Business Impact
For the first time users can understand the business impact of bad data and how it can affect the company’s performance
Start Your Journey With Experts
Case Study
