Have you ever thought about managing your data more effectively? We bet you have! Today, our clients come to us with requests to change how they handle their data. Many of them feel concerned about how to turn it into data-driven insights.
In the modern world, companies are flooded with information. That’s why they face a dilemma of how to use all that data with benefits for their business. Without a solid big data strategy, they risk drowning in data, missing valuable insights, and making poor decisions.
Data overload and operational bottlenecks are inevitable without a well-designed data strategy. You end up with tons of data but lack a clear vision of how to tailor it to your client’s needs and make swift decisions.
With the right big data strategy, your business will sparkle with new colors. From focusing on valuable data and gaining a clear view of your operations to making accurate predictions and getting real-time insights, a quality strategy can fuel your growth.
But how to craft a win-win big data strategy and use it to maximize benefits for your company?
So, we’re here to answer that question and support you in your future endeavors. Keep reading to discover the main steps to building a winning data strategy and understand its role in your business.
Download our Big Data Strategy Checklist now to boost your business efficiency!
What Is a Big Data Strategy?
You’re dealing with large volumes of information that keep growing. Your datasets are so large and complex that traditional data management software can’t handle storing, processing, or analyzing them. If that sounds like you, welcome to the world of big data.
The more data you generate, the more challenging it is to keep up with its amount. You might think, “Enterprise analytics and technologies can be handy.” That’s true, but big data is worthless without an effective strategy.
A big data strategy explains how data will be used in practice and what type of data you might need to achieve specific business goals. In this case, the data explosion won’t be a scary thing if you’re armed with a well-designed data strategy.
Why Your Company Needs a Big Data Strategy
Each day, we generate approximately 2,5 quintillion bytes of data. This shows just how data-driven our world has become. The big data analytics market is predicted to reach $349,56 billion. So, this is a great chance to profit from your data.
No wonder we’re seeing such a data explosion. With over 91% of organizations investing in big data, it’s clear that businesses recognize the value of a data strategy for further growth.
So, let’s reveal why your company may need a big data strategy and what benefits you’ll get if you design it accordingly.
Data-driven decisions
50% of companies report that their decision-making process is data-driven, which means that there are still many organizations that fail to focus on data. Without a big data strategy, they don’t know what to do with their data and use it for more efficient decisions.
Marketing has already recognized data as the key to success. Marketers can create better plans using constant insights from different data sources. In sales, an efficient big data strategy provides more accurate sales forecasts. It helps the sales team use their time better, focusing on clients who are most likely to buy.
More efficient operations
No doubt, almost all companies want to make their operations more efficient through data-driven strategies. Finding and processing the relevant data can take employees hours or even days.
Big data strategy helps streamline operations and spot inefficiencies. Whether it’s customer service, warehouse management, inventory management, or sales, every department can benefit from better processes enabled by a solid big data strategy.
Customer-focused approach
According to the latest statistics, companies that focus on customer experience see an 80% increase in revenue. Big data strategy helps map and track what customers do, need, and want across their interactions and transactions. This gives companies a clear picture of customer experiences and helps them understand their audience better.
When combined with analytics, big data reveals significant details like customer loyalty, revenue, and satisfaction. However, the more personal data is collected, the more users worry about their privacy. That’s why it’s necessary to care about data protection when using big data.
Competitive advantage
In today’s competitive business world, having an edge is crucial. Big data analytics helps businesses understand competitors’ strategies, market trends, and customer preferences. This info lets companies make actionable, data-driven decisions that keep them ahead.
For example, in retail, big data strategy lets you analyze competitor pricing, spot market gaps, and create targeted marketing campaigns. By staying informed and proactive, businesses can outperform competitors and seize new opportunities.
Want to make the most of your data and boost your business? Contact our team to build a winning big data strategy that drives growth and efficiency.
Six Key Elements of an Effective Big Data Strategy
Crafting a robust data strategy requires considering various components to ensure they align with business goals and efficient data management. Let’s explore the key elements in detail:
- Business goals and objectives: Your big data strategy should include specific, measurable objectives to achieve with data. For example, you may want to reduce operational costs, enhance customer satisfaction, or discover business opportunities. The main thing is to understand how big data can help realize these goals.
- Data requirements: Define what type of data you need to achieve established goals. Then, plan how you will source and store your data.
- Use cases and applications: Don’t forget to define a range of use cases and applications of big data, such as customer segmentation, sales forecasting, and social media monitoring. Focus first on your pain points to start tackling them with a big data strategy.
- Data governance policy: Your big data strategy should be based on a governance policy to ensure your data is consistent and reliable. Good governance helps avoid data silos and inconsistencies.
- Data privacy and security: Data privacy and security are crucial parts of a big data strategy. A good strategy includes data encryption, regular audits, and incident response plans.
- Data architecture: A well-designed data architecture is essential for storing, integrating, and retrieving data. This involves choosing between data lakes, data warehouses, or hybrid solutions based on your needs.
Now you’re aware of the essential components of your data strategy. So, it’s high time to find out how to build a big data strategy for your business.
How to Build a Big Data Strategy: Essential Steps
Building a big data strategy is a challenging process that requires a detailed roadmap. So, here are steps to consider when crafting a win-win big data strategy:
Step 1: Understand your business goals
When you start crafting a big data strategy, it can be tempting to try to create a big data strategy roadmap right away. But begin with getting a clear idea of what you intend to achieve with the strategy implementation.
Here are a few tips to make that process easier:
- Conduct a SWOT analysis
- Assess market needs and trends
- Conduct risk assessment
Setting clear business objectives is the foundation of your strategy, ensuring every step and decision aligns with your company’s goals.
Step 2: Proceed with the initial assessment and scoping
The next step is to evaluate the scope of a big data strategy. For this, you’ll need to identify the technology and tools required and set clear milestones. Your assessment will determine how to use the current data landscape and establish the scope of your strategy.
Make sure to review regulatory and compliance policies, as legal issues later can be a hassle.
Step 3: Integrate your big data strategy with the business strategy
The next part of the puzzle to put in place is to connect your big data strategy with the business strategy. This integration is essential for achieving your business goals. Then, review your business objectives to ensure they are aligned with the data strategy.
When both strategies are connected it allows for higher efficiency and more data-driven decision-making.
Step 4: Identify sources of big data
Next, you should identify sources of big data. It’s important to consider data needs, analytics, and internal and external data sources. Additionally, decide whether to use real-time data.
Step 5: Create a data strategy roadmap
At this point, you’ll have identified sources of big data. All that’s left to do is to create a data strategy roadmap. This is your detailed plan on how to access, manage, and store your data. Your roadmap should include situational analysis, strategic objectives, implementation timeline, and monitoring and evaluation.
Contact us today to harness the power of your data. Let’s turn your data into actionable insights and propel your company forward.
Forbytes’ Experience in Building a Data Strategy for Business
Our client, Bygghemma Group, is a leading Nordic online retailer of home improvement and garden products. They offer a wide range of items through various e-commerce platforms and have expanded to 25 countries, becoming a key player in the European home improvement market.
As the volume of data significantly increased, the company faced the following challenges. Their data was fragmented and hard to store and manage. They wanted not only to store data but also to extract practical value from it. Plus, due to changes in Google Analytics, the company needed to preserve all historical data and prevent data loss. Finally, our client sought a data management solution easy to scale.
Therefore, Bygghemma Group turned to Forbytes to build a data strategy for their business and help manage their data. See how we approached our client’s request.
First, Forbytes migrated data from all sources used by the client to a data warehouse. This step was needed to securely store, manage, and stream data in real-time.
Second, we designed a role-based data management space. This setup allowed managers to use data for AI-powered analysis, trend forecasting, gap detection, and more.
Third, our team preserved all historical data by moving it from Google Analytics to BigQuery. This solution was more effective for handling large datasets.
Finally, we enabled the solution to integrate with many tools and systems. This capability allowed for extracting growing volumes of data as the client’s business scales.
Final Thoughts
Remember that creating a successful big data strategy requires more than business skills and technical expertise. It’s about understanding your goals and how diverse data can help achieve them. As you move forward, remember that fueling your business with a big data strategy will make it more efficient and responsive to clients’ needs.
At Forbytes, we provide data engineering services and solutions to help you structure, manage, and optimize data to streamline operations and propel your business forward. We offer everything from data engineering consulting to advanced data solutions.
Want to enhance your data strategy or start from scratch? Contact us for a free consultation to see how our data engineering techniques can transform your business insights and decision-making.
