August 29, 2021
Best Practices for Managing Big Data Analytics Initiatives
According to Forrester, insight-driven businesses are growing at an annual rate of 30% on average and are on track to generate $1.8 trillion in revenue by 2021. While many organizations believe they’re in that data-driven group, few have truly mastered the art of leveraging Big Data to gain a competitive edge and hit high-level objectives. Ultimately, success depends on starting with a strong foundation in which culture and strategic goals take precedence over the latest AI-enabled tool.
In this article, we go over some best practices for managing Big Data analytics programs and avoiding the chaos that comes with poor planning.
[Data Analytics Best Practices Infographic]
Define Your Big Data Strategy
A Deloitte study found that few consumer-packaged-goods firms have mastered “small data.” While this report focuses on one industry, it illustrates a critical point—you can’t build a Big Data strategy if you’re still struggling to capitalize on small, structured data sets.
Without a tight focus on solving a specific problem, managing a Big Data initiative can get complicated fast. To set the stage for success, your data strategy must become your business strategy.
Start by identifying high-level business objectives and potential use cases. What data will you need to achieve those goals? What stakeholders need to be involved, and what are their roles and responsibilities within the context of this project?
Avoid the impulse to collect as much data as possible. As MIT Sloan points out, focusing on capturing data before there’s a strategy in place puts brands at risk. You can’t make data-driven business decisions when you can’t verify whether that data can be trusted.
Identify Useful Data Islands and Eliminate Silos
As you begin to implement your strategy, identify which data “islands” contain valuable insights that could help you streamline your processes or deliver the perfect solution to your customers.
Then incorporate those data sources into a “single source of truth.”
According to Jeanne Ross, director of MIT Sloan School’s Center for Information Systems Research, that unified view is more about alignment than accuracy. Essentially, establishing alignment early in the process allows organizations to develop a shared language for discussing strategic initiatives and defining how to measure success.
Make sure you (and your team) understand the difference between data and information. While it may sound like an issue of semantics, the distinction is an important one. Not all data can be extracted and turned into action. You want to avoid incorporating data islands that don’t add value.
Managing Big Data Analytics Projects Is a Collaborative Effort
Big Data analytics is a team sport. Data science, IT, and other stakeholders need to align on goals, which means that organizations need to create an environment where collaboration and Agile practices are baked into the culture.
We say this all the time: culture is one of the hardest parts of taking on a large-scale transformation. This holds true whether you’re embracing the Internet of Things (IoT), migrating to microservices, or developing a Big Data analytics strategy that sets your brand up for success.
Make Data Accessible
Given the complexities associated with managing Big Data, there’s a growing shortage of professionals with data science and IT skills. These professionals are needed to help businesses make the most out of their growing data sets.
First, organizations need to make it easy for employees to find the information they need. What’s more, those employees need the relevant context to fully understand the data and use it to make informed decisions.
Employees need ongoing training to ensure they’re finding and leveraging the right information. Employers also need to think about assembling a tech stack that makes life easier on their employees—enhancing their jobs, rather than adding an extra burden. Additionally, leaders should provide secure access to large, high-quality data sets to encourage experimentation and discovery among employees outside of the IT department.
Fix Data-Access Issues
When data lives in isolated systems, it’s impossible to use siloed information to improve decision-making, streamline operations, or gain a big-picture assessment of what’s happening inside an organization. So tear down boundaries between data science and the rest of the organization—by creating centers of excellence for sharing knowledge, proof of concept results, and data sets that cross department lines.
Offer Just-in-Time Training & Ongoing Skills Development
Forrester researchers found that top firms prioritize data literacy initiatives—40% of firms are launching programs for helping everyone in the organization make sense of all this data as well as make sure that employees are trained to maintain privacy and security standards.
- Help others begin to use data for the first time in their job
- Provide education and mentoring to bring less-technical employees up to speed
- Hire based on actual skills
- Adjust company culture so that staff will justify decisions they make based on data
Provide Tools for Helping the Entire Organization Work with Data
Teams need data analytics tools that simplify data prep and analytic tasks, and to save time. Look for solutions that allow users to reuse reports and templates.
It’s also important that users can pull from connected data sources to create predictive models and run ad-hoc queries on-demand. AI, natural language processing, machine learning capabilities, and automation streamline processes and offer insights impossible for humans to detect on their own. Many tools also help explain relationships between data points as well as offer visualizations that make it easy for non-technical professionals to arrive at conclusions and make informed decisions.
This might not be the most exciting part of the data analytics process and best practices, but it will protect your brand from compliance breaches and audits. As more people in an organization access data sets, build models, and run queries, you need to make sure you have the right data governance practices to maintain the integrity of your data and models. Governance is critical as it allows organizations to set rules, permissions, and policies to protect your data via automated workflows.
A few areas to focus on:
- Data catalogs and dictionaries. A centralized dictionary helps organizations categorize, tag, and organize data for easy access. It also helps users identify metadata from existing data sets and ensures everyone is on the same page when it comes to business terms, descriptions, and conditions. While this may seem like a small thing, the common language eliminates confusion, encourages collaboration, and maintains consistency across various platforms and databases.
- Data lineage. Data lineage provides an audit trail for data, allowing businesses to track data movements, identify relationships to other data, and reveal which users and tools have access to information and why. Data lineage also plays a critical role in maintaining GDPR, HIPAA, and CCPA compliance, especially as data sets continue to grow exponentially. Additionally, lineage plays an important role in AI applications, particularly in areas like deep learning, machine learning, and neural networks. Data lineage records can help systems learn complex patterns based on human interaction data, providing a faster path to full-automation.
- Model management. Consider using a tool that automates model monitoring processes and can send alerts when a model begins to degrade. You want to avoid any potential scenario where you make decisions on a model that no longer works—especially if you use modeling to determine something like patient outcomes.
Keep Evolving Your Data Analytics Process and Best Practices
Big Data continues to grow exponentially and isn’t likely to slow down anytime soon. Data strategies should be treated as living documents, while cultures should show continuous improvement.
Again, you want to make sure you stay focused on those same goals you defined at the start of this process. Ask yourself (and your team) the following questions to benchmark your progress:
- Which metrics represent success?
- How are you tracking progress?
- What can you do to evolve this strategy?
- Are there opportunities to add new data sets to existing strategies to provide even richer insights?
- Could your team be more efficient or productive?
Over time, you will begin to introduce new data hubs, automate more processes, and address the biggest problems facing your business and your industry. You might also adjust your reporting process, add new metrics, and ditch data sets deemed ineffective.