Consulting Service Category: Data

Data

Data Monetization

Monetize your data

All companies, no matter what they do, are essentially data companies. And almost all of them have access to troves of information on their supply chains, operations, strategic partners, customers, and competitors. But while most companies are leaving money on the table, only a handful are using this information to its fullest extent. By tapping into the insights that can be derived from this information, though, companies can improve every aspect of their businesses—from customer segmentation and demand and churn prediction to pricing optimization and retention marketing. And because these insights can also be sold externally for even greater margins, they’re increasingly regarded as a competitive edge.

 

There are two primary strategies for monetizing data. The first is internal, in which companies work to enhance their operations by using data to improve productivity, make better products and services, and serve customers more effectively. The second strategy involves creating new revenue streams by making data available to partners and customers.

 

Some organizations are able to engage in both. They develop internal revenue streams by leveraging data for operational and customer service purposes, and they also anonymize and aggregate the data for their partners and clients.

Data Strategy

Define a long term Data strategy

Data is the most critical resource for enterprises in the digital era. Enterprises need to develop their business strategies and data strategies together to form a complete resource optimization strategy.

 

Making use of the full potential of enterprise data needs a holistic approach, paying attention to both the process and the people behind it. Improving data management capabilities will drive an organization’s business forward, and help to reach its goals faster. As circumstances change, the data strategy should remain flexible enough so that you can react appropriately.

 

With data security such a hot-button issue in today’s world, companies are reconsidering how much data they can responsibly use to boost sales and services without endangering consumers.

 

Why is Data Strategy so important

 

Unlock the power of data:

Today, there is a greater understanding of how valuable data can be. Individuals and organizations are learning how to gather and extract insights from it. They are learning how to convert data into actions, which can improve organizational performance. With the right data strategy in place, it is easier to collect the right data, obtain insights and make decisions based on them.

 

Increasing Data Volume:

Data is increasingly becoming a valuable asset for companies. The amount of data in the world is growing rapidly and so is the number of challenges companies face in managing it. In the past, some companies may have been able to manage their data by using common knowledge. For example, developers needed data and would just contact the person who created it. While this was an informal approach, it didn’t take too much time because there weren’t many developers and not much data for them to use, but as more data becomes available, companies will need to implement an efficient system in order to keep track of it all and make sure that it is used correctly.

 

Improve Data Management:

A point-by-point approach to resolving data-related issues may work in the short term, but this approach doesn’t address the root cause of data issues and isn’t efficient for solving more complicated issues that span department and project boundaries.

The need to access data and use it within an organization is cross-cutting, meaning it affects every group and department at every level. To be more effective in the use of data across an entire company, a cross-departmental solution is necessary. Creating a cross-departmental strategy for data management will improve how departments work together, for the benefit of the whole organization.

 

Data Governance

Govern your Data

Delivering a robust and scalable data governance framework that supports enterprise data analytics requires a disciplined, holistic approach. Often, enterprise-wide data governance programs are designed in a siloed manner, with little to no consideration for the broader organizational context. This approach can yield short-lived solutions that do not meet the needs of their intended users or are not conducive to successful execution of the overarching business strategy.

We are in a new wave of data governance that is gaining momentum: Agile data governance. This is oriented to improve effectiveness through data literacy of business users who are much closer to the data. It is the antithesis of traditional data governance, which creates a costly and time-consuming process, with rigid policies that do not often work well and that nobody adheres to.

Agile data governance is supported by tools that deliver knowledge about the data to the data users, which differ from the tools supporting traditional governance in that they have machine learning and/or artificial intelligence features built into them. 

As agile data governance becomes more popular, it will likely be adopted throughout organizations. Allowing for different roles to be involved, new processes, and new technology will benefit those using agile data governance with increased capabilities.

Guiding principles for Agile Data Governance

  1. Data governance must be prioritized in the context of business objectives
  2. Agile data governance must balance both offensive and defensive use cases of data
  3. Implementation of a Minimum Viable Capability that is scalable and aligned with the organization’s data strategy
  4. Agile data governance should follow a non-invasive approach leveraging existing structure as much as possible
  5. Data governance and agile development teams must collaborate and understand each other’s philosophy. Agile teams should understand that data consumption much adhere to data governance policy, whereas Data Governance team must understand the concept of rapid incremental development
  6. Senior leaders should understand and actively promote the importance of data governance by not just advocating but also providing adequate resources

Data Management

Use your data to your advantage

 

Data management is a practice that allows organizations to collect, store, and use data in an efficient and secure way to maximize business value. A robust data strategy enables organizations to make informed decisions that maximize the benefits of their intangible assets.

 

These days, organizations need a data solution that provides an efficient way to manage data across a diverse but unified data tier—a complex array of platforms, appliances and services that can include databases, data lakes and data warehouses, big data management systems, advanced analytics and whatever else the leadership decides is necessary.

 

All of these components work together to form a “data utility” that provides the data management necessary for an organization’s apps, as well as the algorithms and analytics that use it. Although current database tools help DBAs automate many tasks, manual intervention is still common because of the size and complexity of most deployments. When a DBA has to make changes manually, there’s a greater chance for errors. Reducing this need for manual data management is a key objective of a new data solution.

 

Within today’s business climate, data has become a valuable commodity. Digital startups and disruptors have long since used data to identify trends and make decisions, enabling them to act before their competitors. As a result, companies are now actively seeking ways to derive value from the new package of information they possess.