Data has become one of the most valuable assets for enterprises operating across practically every industry, from financial services and retail to manufacturing and energy. In a survey of industry leaders, 48.5% of respondents said they’re going to use data to drive innovation in their sectors in 2021.
Big Data is at the center of this trend. A field where the focus is on developing analysis methods for data sets that are too large or complex to be handled by traditional data-processing software.
The annual revenue from the global Big Data and business analytics market is expected to reach $274.3 billion by 2022.
Big Data Analytics is on its way to becoming a real gamechanger for enterprises looking to keep up with the digital economy and rapidly changing market realities.
Why is Big Data data Analytics so important?
1. The scale of data (and analytics) is on the rise
Every day, humans produce 2.5 quintillion bytes of new data – trends like mobility or the Internet of Things (IoT) fuel this trend. A typical organization includes several digital channels in its operation – not only customer-facing channels like e-commerce applications but also back-office systems that play a critical role in production and delivery. All of these channels generate data.
To take advantage of this data, enterprises need to invest in specialized Big Data solutions capable of processing such massive data volumes. Otherwise, they risk missing out on opportunities for building their competitive advantage (for example, by providing a personalized experience to customers).
2. Data is the central pillar of data-driven business transformation
To compete on the market and address the rapidly changing customer demands, enterprises need to build new digital capabilities increase data-driven maturity. And every data-driven business transformation relies on the organization’s capability to collect, clean, process, and analyze data.
According to BCG research, successful digital transformation generated 180% higher earnings growth than among lagging companies in 2020.
3. Data is the gold of 21st century
Regardless of how their revenue is generated, enterprises need reliable methods for collecting, storing, and managing. Otherwise, they can’t improve their business processes to deliver better results.
Companies that don’t use their data to gain insight into the workings of their business won’t succeed in the modern, data-driven world. They need to understand data to thrive in modern economies.
Key considerations for implementing Big Data analytics
Navigating the numerous Big Data technologies is challenging
Experts define Big Data using three ‘V’ terms that come with unique technical challenges:
- Volume – massive data sets pose significant technological demands on the processing, monitoring, and storage.
- Velocity – many organizations generate new data fast and need to respond to activities in real-time. Big Data demands this type of velocity especially from companies involved in technologies around social media platforms, the Internet of Things, and e-commerce.
- Variety – in Big Data, the data’s variety in formats poses another challenge. Big Data storages include word processing documents, email messages, presentations, images, videos, and other formats.
Cloud vs. On-prem data ecosystems
Big Data adoption initiatives involve many expenses. One of which is an extremely high cost of infrastructure set-up to support big data analytics.
Organizations that opt for an on-premises solution need to cover the costs of hardware, workforce (such as new administrator and developer roles), utilities used to maintain the data center, and the development of new software. Organizations with strict security requirements typically pick on-premises setups.
A cloud-based Big Data solution requires hiring specialists and paying for cloud services, as well as setup and maintenance of frameworks required for Big Data solution development. Companies that need flexibility usually opt for the cloud.
A hybrid option can be a cost-effective solution. Some data would be stored and processed in the cloud while other on-premises.
Leverage self-service with proper data governance
Implementing self-service data analytics in your operations without considering the governance framework can become a costly mistake.
Today, data compliance is a matter of interest to every company as more and more governments are passing new laws that regulate access and processing. GDPR is an example of one of the most comprehensive laws on data access and usage.
Without clear data governance standards in place, verifying whether an enterprise is breaking compliance laws is difficult. Data governance also impacts the overall data quality. Without a governance framework in place, the self-service data analytics platform might end up processing poor quality data.
What does Big Data analytics mean for organizations?
Here’s how enterprises are using the potential of Big Data analytics to improve their operational efficiency and make their workforce more productive:
- Create enablers – data analytics insights can be used by any team across any department, creating a solid foundation for decision-making and taking action.
- Leverage data platforms – teams can access data via user-friendly platforms that allow implementing data-driven business transformation.
- Develop analytics environment – Big Data analytics can become the core element of the enterprise analytics environment.
- Visualize insights – analytics solutions come with powerful visualization tools that make insights available to non-technical teams and stakeholders.
- Up-skill your team in each pillar – data analytics offers an opportunity for supporting the professional development of your workforce.
Think Big and start small
Ignoring such data-driven solutions closes the doors to many opportunities – most importantly, for redefining your competitive advantage.
Understanding your current situation in the data analytics journey is the first important step. The maturity of your data ecosystem is a snapshot of your current success in a variety of areas, from financial results to customer service.
Created by a Subject Matter Expert in the area of Big Data analytics, the Data-Driven Maturity Model offers a comprehensive view of your data maturity level, allowing you to identify key areas for improvement in your data ecosystem. It’s a must-have for organizations looking to implement data-driven decision-making across all teams.
Benefits of the Data-Driven Maturity Model
Learn about the components of a high-quality, comprehensive data ecosystem.
- Investigate each cornerstone of the data-driven strategy from top to bottom.
- Identify vital areas for change in order to achieve significant short-term gains.
- Conduct a gap analysis and concentrate on meeting the requirements for a perfect framework.
New opportunities brought by Data-Driven Maturity Model
Intelligent data management
- Can your employees properly store, exchange, and comprehend data?
- Is your data environment safe and reliable?
Find out more about your data protection plans and make sure your data environment is safe and compliant with your policies and standards.
Boost your governance
- Is your business ready to make data-driven decisions?
- Do data story teams have access to a secure and resilient IT ecosystem?
Examine whether the policies, processes, tasks, and actors in your ecosystem assist you in avoiding data swamps.
Increase the effectiveness of your team
- Is it possible for employeesto evaluate data while still having the ability to be creative?
- Is your data plan helping them to be more productive?
Check that your employees can access, monitor, and even alter data freely and independently in order to take advantage of cutting-edge technology like machine learning.
Get stakeholder buy-in
- Are all stakeholders on board with the data management strategy?
- How confident is the team in your data management plan?
Make sure the data environment has a consistent vision and path. Allow for data-driven decision-making and be accessible.
Big Data analytics is the cornerstone of a data-driven strategy for a modern organization. Fall behid this trend and embrace the power of data by taking a good look at your current data maturity level.
Get in touch with us to try our Data-Driven Maturity Model. Think Big, start small.
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