Modernization isn’t just the newest buzzword—although it seems that everyone has their way of defining it. Data and analytics modernization is also not about a single action or implementing some suite of tools. It is rethinking how you use data and analytics as a company.
Data modernization is often described as moving to the cloud, but the approach and benefits you realize go far beyond cloud adoption.
Data and analytics modernization requires modern data management principles. It enables your business users to make smarter decisions with advanced analytical capabilities. A modern, cloud-based platform and scalable architecture, as well as next-generation analytics tools, are needed to migrate from legacy databases and architectures.
While it can be a significant investment, data and analytics modernization can pay off in the form of improved performance, efficiency, and policy-making.
Data Analytics Challenges and Roadblocks
Data is being used as a competitive advantage by industries around the world as a result of digital technologies. Legacy systems, however, don’t offer a flexible and agile analytics ecosystem and are incapable of handling the complexity of modern data. Organizations face the following analytics challenges:
Volumes of data are growing exponentially
Currently, businesses and consumers address and exchange data at a much faster rate. Humans create 2.5 quintillion bytes of data every day. Enterprises without adequate tools to perform data analytics at scale and pace often find it difficult to cope with the high volume of data.
The nature of data is heterogeneous
Organizations struggle with analytics due to heterogeneous data formats. Integrating data from multiple sources is a problem for over one-third of them.
Across multiple systems and processes, data is siloed
It is difficult to get a unified, bird’s eye view of legacy systems because data is stored in silos across processes and systems. To make data more accessible and democratic, companies are shifting from on-premises to cloud and hybrid models for better decision-making analytics at scale.
Data talent is scarce
There is a huge skill gap in leveraging technologies for analytics and making the most of your data stack. Finding the right set of individuals to understand your data and analytics scope and use can be a hassle.
To build and develop reports effectively, your team members must be considered in decision-making. Data and analytics modernization is more than just a technology change; it also involves changing your organization’s skillsets.
Therefore, to mitigate the learning curve, your developers need a training and enablement plan in place if they want to move from their legacy platform to a modern analytics solution.
With Ascend Analytics, you can modernize your data and analytics ecosystem to be more scalable, agile, and future-proof.
Ascend Analytics can help you if you are struggling to:
- Embrace new sources of data as they become available
- Make large data sets accessible quickly and easily
- Integrate real-time data
- Analyze business needs in an adaptive manner
- Utilize advanced analytics such as data science and machine learning
- Utilize new analytics technologies and innovations
- Cloud migration
- Provide equal access to data across the organization
The Four Pillars of Data and Analytics Modernization
1. Data Strategy
The foundation for any data and analytics initiative is the data strategy. It’s the starting point for anything you do going forward. This document will guide your organization on how you approach data and analytics, not only from a technical perspective but also from a people and process perspective. It’ll help you answer questions such as
- How can employees utilize data more effectively?
- To ensure high-quality and accessible data, what processes are required?
- How can data be shared, stored, and analyzed?
- Is good quality data needed? Where can it be sourced?
Investing in data and analytics modernization should be considered a long-term project with high stakes. If you already have a data strategy in place, now is a good time to reexamine it and make sure it aligns with any changes you have made to your business’s goals and how data will help you accomplish them.
2. Data Architecture
You need a cloud-based, agile data backbone to access large volumes of data and different data sources more easily, faster, and more flexible. Choose a data architecture that expands with your data needs over time based on your current needs. A modern data architecture includes:
Less regulated, less latent paths to data:
Modern data architectures allow data to travel in less governed paths, such as through a data lake or persistent staging layer, rather than through a data warehouse.
Data warehouse and data lake
Streaming data and doing real-time analytics can be done with a data warehouse, which is a central component of modern data architecture. Your data needs to be brought together, subjected to business logic governance, and made available in pre-curated formats. It may be a more latent approach, but with a data warehouse, you can stream data and do real-time analysis.
In a modern data architecture, raw data can also go through a data lake, where the data is not governed and can be stored in large volumes without defined use.
By storing some data in a less governed way, companies can innovate and be more agile because it does not necessarily require quality checks, a certain structure, type, or format constraint on load.
Taking a modular approach:
When new approaches or technologies emerge, you can be more resilient and opportunistic if you design your solution with independent components (and play well with each other).
You can adopt new solutions that will benefit your organization without much rework or re-architecture if you don’t fall victim to vendor lock.
Modern data architecture cannot be one-size-fits-all, and not all organizations need the same data architecture.
3. Data Management
Data management is often mistaken for data architecture, but that’s just one aspect of modern data management. For your data to be accurate, it must be available at the right time to the right people. Even though technology and architecture are important parts of data management, you must have a set of principles guiding your data and analytics modernization campaigns.
There are several principles to consider:
One place for all your data:
The big picture can be seen when data from different sources and systems are combined. To gain more context, you can take different approaches today that allow you to be able to bring all your data together. Additionally, you can reduce the risk associated with business users connecting directly to source systems for data collection.
Do you have a fast turnaround for making new data and information available to your users? Are you able to take advantage of new technologies and innovations?
Management of risks:
Take a holistic approach to data quality that spans the entire data lifecycle to minimize the risk of poor data-driven decisions.
Security, stability, and scalability:
Do you trust your data to be always accessible, but only to those who need it? Whether your data is stored in the cloud, on-premise, or a combination of both, how can you ensure that your customers have access to your data?
Modern analytics are the vehicles that turn data into meaningful information that can be used to maximize value from data management.
4. Analytics Tools
Take a moment to consider the reports and applications you use to find actionable data on a daily basis. By upgrading to newer, next-generation analytics tools, organizations can gain better analytics capabilities such as embedded analytics, real-time analysis, enhanced collaboration, and more. But how do you pick the right tool when there are so many options to choose from?
Make sure you consider the entire architecture when choosing your analytics tool. Your analytics solution is part of an end-to-end modern data stack, not a standalone tool. Do a bakeoff, take the tool for a test drive, and consider your entire data architecture.
Prioritize short-term goals:
The modernization process often requires companies to migrate years of analytics reporting to a new platform. As part of a migration strategy, you will focus on the short term: which apps and reports can be retired and which will be migrated initially. During this time, review requirements and triage. Do you really need ten years of historical data or just two years?
Prepare a roll-out plan:
Modernization of reporting is equally educational as technical when it comes to reporting. To encourage adoption, a roll-out and training plan is necessary to educate end users on the features and functionality of your new analytics tool. Consider scheduling a boot camp training session to help your end users become familiar with their new analytics tool.
Benefits of Data Modernization
Organizations must make use of the modernized infrastructure to achieve intelligent business transformation. Fortunately, modernizing legacy data and analytics platforms is a benchmark for ‘digital transformation.’
An integrated roadmap aimed at achieving long-term business goals includes optimizing your data to its full potential. It is the use of data to predict market behavior and deliver the best business outcomes that businesses and enterprises achieve with cloud analytics modernization.
Let’s look at some of the benefits of modernizing your analytics framework:
The ability to make rapid decisions based on data
Data is becoming a new currency as the world becomes increasingly connected through digital channels. Companies can make smart decisions based on real-time insights and metrics using data analytics modernization frameworks. An informed business decision fueled by the right data is crucial to its success.
Using analytics, you can discover the value in your data, keep up with customers’ changing needs and behaviors, stay ahead of the latest trends, and recommend the right products more easily.
Integration of data sources and hyper-scalability
The present competitive landscape requires companies to be scalable. With modern analytics approaches, teams can build scalable infrastructures with data analytics integration platforms for short- and long-term business goals.
Through data integration, you can consolidate data from all sources (internal or external) into a single repository, allowing you to automate labor-intensive tasks and focus on growing your business.
Share enterprise knowledge by democratizing data
You must make your data accessible to all decision-makers within your value chain if you want to unlock its true value. By democratizing data, you make it available to everyone, technical and non-technical departments, by bringing siloed data to a single, unified platform.
Data democratization makes information accessible to everyone. Furthermore, companies should invest in technologies that enable employees to interact seamlessly with data and make decisions independently.
You can have difficulty deciphering and utilizing your data to your competitive advantage if you lack the necessary tools and knowledge. You can achieve maximum value from your analytics environment with Ascend Analytics by moving beyond data as a spinoff. With Ascend, you can turn your analytics modernization capabilities into your strongest advantage!