{"id":1083757,"date":"2023-09-25T07:15:24","date_gmt":"2023-09-25T05:15:24","guid":{"rendered":"https:\/\/www.capgemini.com\/?p=957550"},"modified":"2025-03-21T12:04:58","modified_gmt":"2025-03-21T12:04:58","slug":"seeing-the-successful-growth-of-your-business-starts-with-taking-a-deep-look-into-your-data","status":"publish","type":"post","link":"https:\/\/www.capgemini.com\/in-en\/insights\/expert-perspectives\/seeing-the-successful-growth-of-your-business-starts-with-taking-a-deep-look-into-your-data\/","title":{"rendered":"Seeing the successful growth of your business starts with taking a deep look into your data"},"content":{"rendered":"\n

Seeing the successful growth of your business starts with taking a deep look into your data<\/h1><\/div><\/div><\/div><\/div>
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Helen Ristov<\/h5>
Sep 25, 2023<\/h5><\/div><\/div>
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Why a comprehensive maturity assessment is critical in outperforming your market<\/strong> <\/h2>\n\n\n\n

Over the last ten years, we\u2019ve seen an exponential increase in the amount of data captured, stored, and consumed across the globe \u2013 from just 2 PB (petabytes) to now over 150 PB \u2013 with continued projected growth to exceed over 200 PB in the next five years. As the volume of data grows, the infrastructure that supports our data-driven society is being pressed \u2013 not only for innovations in data processing \u2013 but fresh governance and policy paradigms as well.  <\/p>\n\n\n\n

In a rather short period of time, traditional analytics have evolved into new fields of data science and engineering to handle these challenges by utilizing a plethora of tools developed to address growing demands. As with any disruption of this scale, there were pioneers who ventured into the unknown and helped pave the way \u2013 while new governance policies were adapted and refined to fit evolving business needs. <\/p>\n\n\n\n

Many organizations are still operating on antiquated technologies that do not support the necessary functions for advanced analytics and machine learning. Tools and technologies have been created that are optimized to handle large workloads at the speeds needed for near real-time processing. Not all businesses require this level of sophistication, but a data assessment is a good starting point to aggregate the demands of your business and summarize where you are proficient and where you are falling short.  <\/p>\n\n\n\n

Assessing your data maturity <\/strong><\/p>\n\n\n\n

Many organizations are asking themselves how they compare to their industry peers. It\u2019s important to gauge how sophisticated your environments are \u2013 and whether your organization can stay competitive in its respective niche areas. There are different data maturity levels ranging from the basic functions of reporting to data-driven \u2013 where decisions are made in a fully automated way. Various tools can be used or suggested for each category depending on complexity and use cases. However, proceeding with a realistic assessment of your business and your desired outcomes is a good place to start. Your organization can be evaluated and categorized to align the correct technologies and make suggestions that are appropriate for your business.  <\/p>\n\n\n\n

What are some of the most pressing data challenges that modern organizations face?<\/strong> <\/p>\n\n\n\n

The sheer volume of data<\/strong> <\/p>\n\n\n\n

When more data is collected, more monitoring and validation are required to manage the full lifecycle of data. Applications and dashboards that help with data management are becoming increasingly more important in organizing and viewing data through a real-time lens. The pillars to consider when implementing a data lifecycle management solution include data creation, storage, usage, disposition and archival. Many organizations incorporate hot and cold data storage with a retention policy on the cloud to save on costs associated with managing data.  <\/p>\n\n\n\n

Multiple data repositories<\/strong> <\/p>\n\n\n\n

Large organizations may wind up with dozens of business solutions \u2013 each with its own data repository. These could include databases,\u202fCRM software<\/a>, ERP, Cloud Storage, etc. When multiple systems are involved, it\u2019s difficult to break from siloes into a more integrated platform for data-driven decisions. Creating a curated and linked repository should be considered as a top priority for most organizations. <\/p>\n\n\n\n

Data quality<\/strong> <\/p>\n\n\n\n

The amount of data passing through multiple data storages (and throughout an entire organization) can lead to a host of data issues such as naming conventions and field types that can be different for the same field. Cases like these can often be rectified using data catalogues and crawlers to standardize variables through common names. In addition, data may be out of date, incorrect, or malfunctioning. Making judgments based on this sort of data might result in your firm losing a lot of money every year.  <\/p>\n\n\n\n

Data integration<\/strong> <\/p>\n\n\n\n

Data integration is the process of combining disparate data sources into a common view \u2013 and often helps improve data quality and synchronization issues. Companies with mature data integration processes often see improved operational efficiency and more valuable insights gained by aligning their systems. Building a common data model eases future integrations because all integration processes will speak the same language.  <\/p>\n\n\n\n

Data governance<\/strong> <\/p>\n\n\n\n

Comprehensive data policies are essential in effectively keeping track of your changing ecosystem. To attain the value and outcomes you seek, it is critical to build a data governance foundation around trust, transparency and ethics, risk mitigation and security, education and training, collaboration and shared culture, and accountability and decision rights.  <\/p>\n\n\n\n

Data analysis and automation \u2013 Supporting your key business cases with trusted analytics<\/strong> <\/p>\n\n\n\n

Valuable insights are needed to drive effective executive decisions. The reliability of your insights is only as solid as your data and supporting systems. The end goal of your data infrastructure should be to support your key business cases with trusted analytics. Incorporating data processes that automate reports, insights, and forecasts will streamline your operations and enable employees to spend their time deriving value from reports \u2013 instead of data scrubbing. As an example, pipelines can be created to automate your typical data transformation jobs.  <\/p>\n\n\n\n

Moving from a nascent and siloed data function to a data-driven organization starts with a comprehensive data maturity assessment <\/strong> <\/p>\n\n\n\n

An effective assessment can also help recognize where you are struggling the most within your business today \u2013 and help provide corrective actions and recommendations to address these concerns.  <\/p>\n\n\n\n

It can also aid you in determining the scope of your data transformation. Identifying the correct KPIs to track progress is a valiant effort but can be objectively difficult \u2013 they should be closely aligned with business objectives. Management will want to know if their investments are paying off \u2013 and picking the correct measure is key here. For a data transformation project, I would suggest the following categories and KPIs to help you measure success: <\/p>\n\n\n\n