11 Tenets to Realize and Unlock the Transformational Value of Data!.
- Suresh M
- Nov 26, 2024
- 7 min read
Discover the steps to becoming a data-driven enterprise…..
1. DATA IS THE SOURCE OF BUSINESS VALUE:
In a world where businesses are constantly flooded with information, the ones that really succeed are those who see data as a treasure rather than just noise or input for the process.
Our HR department embraces an Application Tracking System (ATS) built on past hiring experiences, which was helpful. Meanwhile, the finance team gets smarter at predicting cost fluctuations. Our cafeteria even stepping up its game by planning meals based on employee’s preferences.
It’s amazing how data can bring us all closer together!
2. DATA EXPERIMENTS NEED A PROBLEM STATEMENT:
In a world where businesses are constantly flooded with information, the ones that really succeed are those who see data as a treasure rather than just noise or input for the process. Data can help solve the business problems.
Business Cases are the problem statements. Every enterprise has its own strengths, opportunities, challenges, and threats. Enterprises need a roadmap that outlines their current state and what is required to continue reaching their strategic goals. Some of them are:
Identifying the customer market segment for the products launched can boost the product’s success post-launch. Understanding customer behavior patterns and targeting for hyper-personalized offers rather than generic campaigns can help double conversion rates. General Physicians handle hundreds of patients with different complaints in a single day. Any Data-based assistance to the GP is a great help in diagnosing the patients.
How AI helps to detect data packet Anomalies in the Network ?
Discover what pulls us back on a network packets and how AI models helps us to ensure safer networks!
AI Use Cases in the Medical Diagnosis
Discover the AI Use Cases in the HealthCare System
Is our Learning process really productive ?Are we learning the right way ?
This story talks about the different types that helps to learn effectively that can improve the learning process and be…
3. DATA HELPS TO BUILD A PRODUCT THAT SOLVES PROBLEMS:
Now we have datasets and a problem to solve; the immediate question is: How to solve the problem? We need a product to solve the problem; in Other words, we need to build a product that can produce value to solve a business case or problem.
Let’s take a tech company with a lean customer care team. The number of queries to handle in a minute is thousands. Considering the volume of queries, the customer care team can’t handle all of them. We may need a chatbot product to handle level 1 support addressing all the queries. It is predicted that level 1 support can reduce the traffic to 30%, which the current customer care team should be able to handle.
Following the product development lifecycle, the following are worth considering to ensure the final product works as expected.
Define the result: Product Acceptance Criteria. Leverage the data: Use historical data to optimize the responses. Measure success: Track Query handling efficiency, time taken, and user satisfaction.
4. USE AGILE WAY OF PRODUCT DEVELOPMENT:
As the Agile way of working is inevitable for product development, Working with Agile reaps the following benefits.
Break the work into sprints: Short, focused development cycles of 1–3 weeks. This helps with the MVP (Minimum Viable Product Delivery) so that the product can generate value within a month of launch. MVP is the short-term goal for making the business deploy its products in the market for revenue generation. Agile always focuses on both short-term and long-term goals. Iterate continuously: Deliver working software after every sprint for feedback. Collaborate daily: Hold stand-ups to ensure alignment and address blockers. Be flexible. Respond to changes quickly and address feedback and comments. This will increase the chance of the product’s success and effectiveness.
For Example, the chatbot expects to handle 10 queries; the product can be launched once it is ready with 5 questions (in 1–3 weeks) and can follow the improvement cycle.
5. OPEN UP DATA ACCESS WHILE MAINTAINING DATA SECURITY:
As the title above says, Data Democracy is an important concept that makes an enterprise “Digital”. At the same time, data security must also be considered equally so that data assets can be protected from theft, leakage, and misuse by anyone.
Here are some interesting points:
Data Classification: Every dataset can be categorized into tiers:
Public: Non-sensitive data available to everyone. Internal Use: Data is safe for general employees but not for public viewing. Restricted: Highly sensitive data requiring strict access controls. Role-Based Access: Each employee’s role determines access to data. Anonymization and Masking: Personal details need to be anonymized to protect customer identities. Real-time masking tools ensure that even sensitive fields, like credit card numbers, are only visible to those with explicit clearance. Audit Trails: Whenever someone accesses the data, this event needs to be logged. This helps us stay accountable and lets our security teams quickly spot and tackle any unusual activity. It’s all about keeping things safe and secure! Secure Sandboxes: When the team had a virtual environment to experiment with data, the sandbox isolated their work from live systems, ensuring no sensitive data could leak.
6. DATA COMPLIANCE:
Data privacy and compliance are more than just legal requirements; they’re vital for earning trust and ensuring long-term success. When businesses make privacy a part of everything they do, they can turn potential challenges into real advantages. It shows that doing the right thing is an ethical choice and a smart move for success!
Some typical steps in the data compliance process:
Data Mapping and Transparency: We need a Master and Reference data repository and a detailed map of all the data the company collects. This map shows where the data is stored and who has access to it! We need a privacy policy explaining user data use and ensuring transparency. Privacy and data protection regulators exist, such as GDPR, CCPA, PIPEDA, and US FTC compliance principles. Strengthening Security: Data encryption was implemented both at rest and in transit. Multi-factor authentication was added to all employee accounts. Role-based access controls ensure only authorized personnel can access sensitive data (SPII, PII..)Embedding Privacy by Design: The product team adopted a “privacy-first” approach: Anonymization techniques are to be applied to all user data before it is consumed, even for AI model training. New features must undergo privacy impact assessments to identify and mitigate risks before consumption. No bias is introduced due to data consumption or usage.
7. AI CAN BE THE OPTIMAL WAY OF SOLVING COMPLEX BUSINESS PROBLEMS:
AI can be the shortest and optimal way to solve complex challenges and drive innovation.
When leveraged thoughtfully, it doesn’t just improve processes — it accelerates transformation, turning what once seemed impossible into a reality.
AI can analyze millions of data in the shortest possible time and produce impossible insights.
AI is our new tool, comparable to calculators or computers in the past decades.
8. HYPER-PERSONALIZED PRODUCTS ARE POSSIBLE WITH DATA:
Personalization and recommendations are more than just cool features; they’re all about creating meaningful experiences through data analytics. By tapping into user behavior and insights, businesses can offer real value to their customers, making them feel recognized and appreciated. This connection helps build loyalty and a lasting relationship!
Businesses can deliver personal value by leveraging user behavior and insights, creating loyal customers who feel seen and understood.
Some insights on the Personalization & AI Solution Building from the Netflix Use Case for Movie Recommendation:
Data Collection: Analyze user behavior: What shows were watched? What genres do you like? How long does the same user behavior persist? How long do the users watch? Some more details or characteristics of the event: Time of day, device type, and location patterns. Segmentation and Insights: Using the data, they group users into segments: Binge Watchers: Users who finish the entire series quickly. Casual Explorers: Those who pick multiple shows but rarely finish them. Genre Enthusiasts: Fans of specific categories like sci-fi or romance. Recommendation Algorithm: With these insights, the team can build a machine-learning model that combines collaborative filtering (users who liked similar content) and content-based filtering (similarities between shows).
9. DATA INTEGRATION & COLLABORATION EVERYWHERE:
Tackling complex challenges is so much easier when we work together! By breaking down barriers and encouraging collaboration, we can mix our different skills and insights to achieve amazing results. Let’s team up and see what incredible things we can accomplish together!
Integration and Collaboration are crucial in creating a data-driven enterprise. We may need to apply strategies in Master Data Management, Reference Data Management, Metadata Management, Data Architecture & Models, Data Quality, and Data Privacy and Security to create a roadmap for unifying their data systems and workflows. This can easily solve any complex business problem. Remember, as we have seen, data is not just the input source for the process; it is the source of business value.
Centralized Data Platform: One way is to implement a cloud-based (or Proprietary) data lake that allows teams to upload, store, and access data in real-time. Standardized Formats: Each team agree on common data formats and metrics, ensuring their datasets could seamlessly interact. Collaboration Tools: Adopting a suite of collaboration tools, including shared dashboards, live data visualizations, and communication channels tailored for cross-functional discussions.AI Integration: An AI-driven analytics platform can be added to process the integrated data, identifying patterns and generating predictions that no single team could uncover independently.
Let’s take an example of a Retail Store:
The Inventory Team shared real-time data on Supply Availability.
The Demand Team identified patterns, helping to predict when Sales spikes are likely to occur.
The Sales Team used this data to optimize sales through personalized and differential offers.
This is an integrated AI platform that works by combining their expertise and data. The AI model predicted personalized offers, which can be proven with A/B tests yielding 20% more sales.
10. GAIN COMPETITIVE ADVANTAGE WITH DATA:
Getting a competitive edge with data isn’t just about gathering it; it’s all about using that information to speed things up, solve complex problems that would be impossible otherwise, create personalized experiences, and understand what your customers need even better than anyone else. Let’s make data work for you!
11. ORGANIZATION CHANGE & CULTURAL SHIFT:
The support of the Data Governance Team, along with the commitment and backing of the Leadership Team and the CDO or CTO, is essential to transform the enterprise into a fully data-driven organization!
Success always sparks a wonderful cultural shift in any organization. Leadership commitment and Organizational decisions are the key!
We’ve reached the end of this discussion. Next time, we will explore more interesting topics. Stay tuned! 🔍
Thanks for reading!
Do follow, clap, and support so that it will help me to write more. Cheers!
If you like this story and want to subscribe for more, click below https://medium.com/@mskmiba/subscribe
Follow for more such content around data & analytics, Social Interest, and Well-being!
Comments