The Path to Data and Analytics Modernization

data & analytics modernization

Analytics modernization- a term you must have heard one way or another. In today’s digital era, data is the most valuable asset for any company. Companies now are focusing on using their data to perform better analysis, make fast, error-free decisions, and meet customer expectations. Now, every company uses data and analytics to fight the market competition and stay ahead of their user expectations. This guide will help you understand benefits of data modernization and problems, and much more.  

What do you understand by Data and Analytics Modernization?  

The data analytics modernization process goes beyond simply using the latest tools as it involves rethinking the data architecture. Data & analytics modernization strategy is used by enterprises and businesses to access relevant information, perform enhanced analysis, and understand their customer needs to provide tailored solutions. 

Analytics modernization is a vital aspect for companies to effectively take advantage of vast amounts of data available to them. Companies can efficiently access, store, analyze, and transform data with high transparency. Businesses that have advanced their analytics architecture no longer face challenges in storing, activating, and orchestrating different data types. Modern analytics and IT architecture enable the effective combination of different data types from structured and semi-structured to entirely unstructured data sets.  

What are the Benefits of Implementing Data and Analytics Modernization?  

Benefits of Implementing Data and Analytics Modernization
Benefits of Implementing Data

With the increasing amount of data available, businesses need effective and more modernized analytics to act rapidly to meet customer expectations. Data modernization enables businesses to quickly adapt to changes and grow as an organization. Some of the main benefits of implementing data and analytics modernization are:  

  • Scalability and flexibility: Cloud-derived databases often scale to meet increasing analytics requirements. It enables customers to spend their time and efforts on evaluation rather than operational databases.  
  • Integration of new data sources: Companies can rapidly combine new data sources and host the increasing data sources available to them as they need. Through an advanced analytics architecture, companies can rapidly access different data sources, analyze changes, and put in real-time data.  
  • Faster time to insight: It allows the users to quickly find value in data and ingest streaming data to evaluate events as they unfold.  
  • Democratizes access to data: A modern approach helps to store data in one place against siloes of data, allowing users to run reports and share analytics as required securely.  
  • Planning for the future: With an effective data and analytics approach in place, a modern data architecture provides the path for more advanced forms of analytics like machine learning and artificial intelligence.  

On the other hand, legacy tools lack the potential to resolve modern data issues. So, what are the main aspects you should look for when approaching data and analytics? To begin with, you need to understand the several types of modern data and analytics issues your company is facing today and how they affect your organizational growth.  

Insight into Common Modern Data and Analytics Problems  

There are several problems and industrial shifts which deviate organizations from implementing data and analytics modernization. This is because the outdated system does not keep up with the business and technology demand available today. Businesses have:  

  • Exponential data volumes: Companies are facing issues with keeping up with the vast amount of data they are generating, and this volume is increasing rapidly annually. However, this exponential volume and variety of data bring opportunities for companies. There are competitive benefits with the capability to store, analyze, use, access, and transform more data- along with increasing data types, volumes, and sources.  
  • Diverse types of data: There are varieties of data coming up every day, and analytics systems face challenges while managing semi and unstructured data. Marketing, customer engagement, social media, and a variety of different mediums are new forms of data- which can be used to understand customer needs when processed properly. Hence, different forms of data require businesses to adopt new processes and technologies to keep up with the pace.  
  • Variety of consumers: Reporting, excel, and dashboarding are no longer sufficient analysis tools as people are using data for several purposes today. Bi-directional use cases, near real-time, advanced analytics, embedded use cases, and streaming are expanding and becoming more common for companies now.  
  • Cloud, on-prem, and hybrid systems: Data sources have been in the cloud and on-prem now but today companies have operational systems, data warehouses, and source systems split between being hosted hybrid, in multiple clouds, on-prem, or in the cloud which adds to the modern challenges that companies are facing today.  

So, after knowing about these modern business and technological demands, you must be wondering how you can solve modern data problems. The next section will help you understand modern data and analytics pillars.   

The Five Pillars of Data and Analytics Modernization

Pillars of Data and Analytics Modernization
Pillars of Data and Analytics Modernization
  1. Data strategy: It serves as a guide for your company in terms of how the company approaches data and analytics. This does not include the technical view but also the process and people’s perspective. This pillar help companies find the solution to questions like: 
  • What data is required? Where are its sources? How to ensure data quality?  
  • What technology approach will help in sharing, storing, and analyzing data?  
  • What steps are needed to ensure the data is accessible and of high quality?  
  • What do the workers do to effectively use data?  

A company’s data modernization strategy should be reviewed by stakeholders and be seen as a long-term strategy. At any point, you can re-examine your strategy and align it with the company changes and analyze how data can help you achieve your goals.  

  1. Data Architecture: You need a sharp, cloud-derived, future-ready data background that allows easy, quick, and flexible access to the vast amount of data and among various data sources. Some of the advanced data architecture involves: 
  • Alternative to less latent and governed pathways to data: In an advanced data architecture, data can take less governed or less latent path such as a persistent staging layer or a data lake which means all data now does not need to go through an organization data warehouse.  
  • Data lake and data warehouse: A data lake can store several vast amounts of raw data which might not have a defined use. It offers a less governed way as no quality check is required which means data lake provides a way for agility and innovation.  

A data warehouse is a critical element of modern data architecture. With this approach, you can combine distinctive data altogether, apply business logic governance to data, and make it available in pre-curated ways. It provides more opportunities to stream data and do real-time analytics.  

  • Modular approach: By creating your solution with elements that are independent of each other, your company can be highly opportunistic and resilient when building new approaches and technologies.  
  1. Data Management and Governance: Modern data management demands your data be precise and available to the right individuals at the right time. Moreover, architecture and technology play a critical role in data management, but your company needs to have defined principles for your data modernization strategy. These principles include:  
  • Scalability, security, and stability: This involves you deciding whether your data is on the on-prem, cloud, or hybrid landscape.  
  • Risk management: Reduce the risk of poor decisions depending upon poor data using a holistic view of data quality that includes the entire data lifecycle.  
  • Agility: How fast can you make your latest information available to people who need it? Is your company able to take benefit from innovations and technologies that are available?  
  • All data in one location: Can you consolidate data from multiple systems and sources to look at the big picture? Storing all your data in one place helps you to reduce the risk linked with company users connecting to source systems directly to get data.  
  1. Analytics tools: Think of the applications and reports you create daily to find valuable insight from your data. Shifting to new, future-ready analytics tools will offer better analytical potentials such as embedded analytics, real-time analysis, improved collaboration, and so on. The following points should be considered while choosing the most appropriate analytics tool: 
  • Consider your data architecture when choosing your tool 
  • Analyse skills sets 
  • Focus on short-term 
  • Put together a roll-out plan  
  1. The right people and processes: It is crucial to remember to know that data and analytics modernization effort is not just about shifting to technology but also about the skillsets needed within your company. Some significant aspects that should be taken into consideration to ensure a successful migration are: 
  • Training and enablement: This involve two pathways. First, “how to build” learning track, and second, “how do users use it”.  
  • How your company receives training: Understanding your target audience is significant to how developers and users will get training. Knowing how your audience learns best such as through documentation, webinar, classroom training, and so on—will enable you to provide training that enhances value.  
  • Load balancing: There will be a time when your modern analytics tool and legacy reporting will likely both be active at the same time. Knowing the need on time is crucial and distributing the workload effectively across both tools is key.  


You can’t just stare down the path of data and analytics modernization. Every company needs to take a balanced outlook and have a strategy in place to shift to an agile, cloud-based, and next-gen ready data and analytics environment. 


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