Seeing the successful growth of your business starts with taking a deep look into your data

Why a comprehensive maturity assessment is critical in outperforming your market 

Over the last ten years, we’ve seen an exponential increase in the amount of data captured, stored, and consumed across the globe – from just 2 PB (petabytes) to now over 150 PB – 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 – not only for innovations in data processing – but fresh governance and policy paradigms as well.  

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 – while new governance policies were adapted and refined to fit evolving business needs. 

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.  

Assessing your data maturity 

Many organizations are asking themselves how they compare to their industry peers. It’s important to gauge how sophisticated your environments are – 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 – 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.  

What are some of the most pressing data challenges that modern organizations face? 

The sheer volume of data 

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.  

Multiple data repositories 

Large organizations may wind up with dozens of business solutions – each with its own data repository. These could include databases, CRM software, ERP, Cloud Storage, etc. When multiple systems are involved, it’s 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. 

Data quality 

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.  

Data integration 

Data integration is the process of combining disparate data sources into a common view – 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.  

Data governance 

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.  

Data analysis and automation – Supporting your key business cases with trusted analytics 

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 – instead of data scrubbing. As an example, pipelines can be created to automate your typical data transformation jobs.  

Moving from a nascent and siloed data function to a data-driven organization starts with a comprehensive data maturity assessment  

An effective assessment can also help recognize where you are struggling the most within your business today – and help provide corrective actions and recommendations to address these concerns.  

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 – they should be closely aligned with business objectives. Management will want to know if their investments are paying off – 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: 

• Overall: Improvement in operating profit margin 
• Operational Efficiency: Execution speed on data extraction and processing times, reduction of defects and errors and maintenance costs and labor, and increases in system uptime 
• Employee Engagement: Increases in employee productivity, working hours saved, reduction in incidents, and improvements to SLAs 
• Customer Engagement and Satisfaction: Time spent on apps, lead generation, digital marketing KPIs, customer retention, customers registered on site 
• New Sources of Revenue: Customers buying via AI-based recommendations and product revenue associated with new platform features. 

With our ADMnext^Data offering, Capgemini has already conducted several assessments with clients to help them understand their key data challenges by examining support tickets and logs and categorizing them into core problem areas. In utilizing these assessments, we’ve helped a global CPG leader achieve over €30M in savings annually and are currently also considering adapting GenAI initiatives to help us provide root-cause analysis and ticket resolution to suggest the best corrective actions.  

Overall, a complete data assessment can help you measure how you stack up and help you prepare for future workloads as a proficient data-driven organization. And, according to the Capgemini Research Institute, data-driven organizations currently enjoy a performance advantage of between 30% & 90% across customer engagement, top-line benefits, operational efficiency, and cost savings. 

In my next post, I’ll be taking a deeper dive into how you can address the key data challenges outlined above. In the meantime, if you want to get started on your own comprehensive data maturity assessment, drop me a line below.