Business can be summed up as a human process of making, selling, and delivering a product or a service. Enterprise data strategy is very important to the success of any enterprise. Enterprise data strategy is to implement a uniform approach to the management of data throughout the enterprise.

It is to ensure a standard data platform that allows you to grasp an enterprise’s entire information flow. It is to use the same standards and data definitions throughout the organization so all departments can work with the same platform.

Data governance (DG) is the process of managing the availability, usability, integrity, and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn’t get misused. – from Techtarget

Some of the benefits of this are

1. Golden Source of Data – A centralized data storage to be used for all the reporting and analytics purposes. Avoiding the data risk, errors and saving times required for reconciliation of numbers from different report.
2. Rich Meta Data – Unified data attributes and definitions help to maintain rich metadata and logic. This reduces the risk of misinterpretation of data and in turn risky or wrong decision making
3. Security – This also help to implement a standardized Data Security and Data Risk management to ensure data security. Customer personal information can be stored centrally to ensure GDPR followed across all the platforms.
4. Enhanced and Standardized process – It helps to ensure standardization of processes across organizations to ensure accuracy of data
5. Data Quality – THis provide a chance to implement centralized Data Quality Framework across organization
6. Advanced Analytics – Centralized data helps to create a single view of customers, understanding interaction on different platforms and with different products. It helps to apply advanced analytics and machine learning model like building the Customer Propensity Model.

Having a data strategy is an important component of any business. It will allow you to take your company to the next level by ensuring that every department is running smoothly and efficiently. It also promotes a culture of Data-Informed decision-making across organizations. This will help you to streamline your business processes and data management.

Feel free to connect with me on LinkedIn to discuss this further – Sagar Nikam

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This blog targets a section of product managers who would like to build products from sustainability and economic models.

The world is moving towards a more sustainable future. Companies are making efforts to reduce carbon emissions while products are being built and consumed. The consumption of the goods creates an impact on the environment. The goods have a limited life after which they are considered waste. The waste is created by the goods which get disposed of by the consumers who have no value for it. The waste goes to landfills, in oceans, or is burnt. The negative impacts are not just limited to the waste itself, but it also impacts the resources that were used to build the product. There is a better way to

What is Circular Economy?

A circular economy reduces material use, redesigns materials to be less resource-intensive, and recaptures “waste” as a resource to manufacture new materials and products. (source: epa.gov)

Why Circular Economy is Good for the environment?

• Lower Carbon Emission : It reduces carbon emissions by reusing the existing products. Helps to reduce the pollution.
• New revenue models : Innovation and adaptation can lead to new avenue for diversifying the business and promoting new start-ups
• Financial savings and consumer empowerment from repairing goods

Circular Economy in the Digital Product space.

• Review – understand the needs and value addition by the product against the investment
• Reuse – Evaluate currently available solutions or solutions closer to the required product. Its sometimes economical to upgrade current product rather building new one
• Recycle – If you decommissioning any digital product – Evaluate if it can be used for something else or hand it down to startups or students for project experience and learning purposes 
• Rewire – Promote a culture and attitude towards Circular economy. 

Conclusion:

The circular economy is a great model to follow. We are the architects of the products, so it is up to us to build something better for the future.

I hope this blog post has given a brief idea about the Circular Economy and how it can be applied to the Digital space. The key takeaway is to start thinking about how your product can be reused, recycled, and re-engineered. Please feel free to contact us with your thoughts/opinions/ideas on this blog post.

Read the article on how to reduce Data Waste and build a Data-Driven organization

I would love to discuss this further. Feel free to contact me here

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What is Data-Driven?

Data-driven decisions are based on the data collected. Data is so valuable that organizations want to leverage it as much as they can. For example, financial institutions look at data such as credit scores and make lending decisions based on this information. Data-Driven decisions are concerned with analyzing the data and making predictions about what will happen next.

What is Data-Informed?

Data-informed decisions are made with a combination of data and intuition. Data-informed decisions rely on a mix of quantitative and qualitative analysis.

For example, let’s say you’re deciding whether or not to purchase a new piece of machinery for your factory. You could conduct an ROI analysis that would quantify the costs related to the machine and its potential benefits to your company. The qualitative aspect would be whether or not people in the company like the machine, what it will do for their productivity, and how it could help them complete their tasks better. Combining these two aspects will give you more information about what decision to make than just relying on one side of the coin.

What is Data-Augmented?

Data augmentation is the process of using data to fill in gaps when you don’t have sufficient information. Data augmentation can be done manually or with automated software. It’s important to note that while data augmentation helps provide more complete sets of data, it cannot make up for a lack of original data.

Conclusion

Data Manager, Data Product Managers, and Leadership team play a key role in setting the direction for the organization. It’s about leveraging the data you have to make strategic business decisions and focusing on reducing the Data Risk.

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