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Smart organizations use vast amounts of different types of data to better understand their customers, track inventory, improve logistics and operational processes, and make informed business decisions. . Successful organizations also understand the importance of managing the vast amounts of big data they create and finding reliable ways to derive value from it. Having a big data strategy to store, manage, process and apply all data effectively and efficiently is critical.
A well-defined, comprehensive big data strategy lays out what it takes to become a more data-driven organization—a successful organization. It should incorporate guidelines to achieve a data-driven vision and guide the organization toward specific business goals for big data applications. It’s easier said than done, but you can do it by following the four steps outlined here.
What is big data?
Big data isn’t just about size. Data volume is just one of the Vs of big data, and managing it is actually one of the easier challenges to solve. The more difficult challenges of big data have to do with the other Vs. The variety of data types, the speed with which data changes, the accuracy of data from different systems, and other characteristics that make it difficult to process large amounts of constantly changing data.
Big data comes in many forms, including unstructured, semi-structured, and combinations of structured data types. It is also collected from a variety of sources including streaming data systems, sensors, log files, GPS systems, text, images, audio and video files, social networks and traditional databases. Some of these sources add or update data millions of times per minute.
Not all data is created equal. As a result, companies need to ensure that big data sets from various sources are accurate and reliable. This highly variable data may also need to be augmented with additional data from other repositories. For businesses, the ability to handle all these difficult aspects is key to unlocking the power of big data. It starts with a solid strategy.
The Importance of Big Data Strategy in Enterprises
Enterprise data is often stored in silos. It’s nearly impossible for companies to get a comprehensive view of all their data, dumped in data warehouses or trapped in disparate departmental systems with poor data integration. Additionally, both the data quality and reliability of the data sources in the set of big data can vary, which can lead to high storage and associated data management costs.
As a result, building a big data strategy has been put on the back burner as companies are preoccupied with managing and coping with day-to-day business operations. But without a strategy, companies will be dealing with a variety of simultaneous big data activities across the organization. This can lead to duplicate efforts and, worse, competing efforts that are not aligned or clearly do not meet the company’s long-term strategic goals.
What should your big data strategy include?
An effective big data strategy provides a clear roadmap for how data is used to support and improve how business is conducted and the approaches used to manage the big data environment. The plans it contains must be actionable, widely adopted, and based on an enterprise-wide recognition that data is an asset that drives continued business success. The strategy should also specify how to address the above challenges.
The key to creating a successful strategy is not just treating big data as a technical problem. It’s important to talk to business stakeholders and get their feedback. Doing so will ensure that the strategy is adopted. Many aspects of big data management are as much about cultural alignment as technology enablement. Business managers and senior management need to support big data strategies and participate in the process.
How to build a big data strategy
Investing in big data technologies and tools without architectural and structural planning can waste an organization’s time, money, and resources. Here, we present a four-step approach to developing a big data strategy that avoids these negative outcomes.
Step 1. Define your business goals and objectives
To have a successful big data strategy, you must first define the business goals you are trying to achieve. There is no one-size-fits-all answer, because not all businesses are the same. However, you need to ensure that your strategy aligns with your overall enterprise business goals while also addressing key business issues and key performance indicators.
Engage stakeholders (including data management teams, line-of-business leaders, data engineers, data scientists, and anyone else who consumes big data stores) from the outset to provide critical input. continuous base.
Step 2. Identify data sources and assess processes
The next step is to identify a variety of data and assess your organization’s current business processes, data sources, data assets, technology assets, capabilities, and policies.
Once you have identified the sources of your data, perform a data strategy assessment. Make sure you address the business goals outlined in Step 1 and work from there. For example, if the business goal of your data strategy is to improve the customer experience, your current state assessment covers all customer-related business processes, business models, or data assets. It is recommended to interview and involve all relevant employees and stakeholders when assessing the current state.
Step 3. Identify and prioritize big data use cases
don’t let the sea boil Applies here. Start small, think big, iterate often, and think in terms of use cases when developing your big data strategy. Identify big data use cases that meet the business objectives described in Step 1. Use big data analytics to examine large amounts of data to uncover hidden patterns, correlations, and other insights. These exercises will help you build and improve your use cases.
The next step is to start prioritizing these use cases based on factors such as business impact, required budget, and required resources. Depending on the number of departments represented in the process, it can be difficult to narrow down the use cases and prioritize which use cases to start with. Remember to stay focused, write down agreed-upon use cases, and plan as a group.
Step 4. Create a roadmap for your big data project
Once you’ve identified your business goals, understood the state of your data and current capabilities, and identified your use cases, you can start planning your big data roadmap.
This critical step is often the most time consuming step for an organization. When creating your big data roadmap, remember that it’s just an outline. You can iterate and evolve your roadmap over time. With that in mind, sketch out your desired end state and work backwards to make sure your end goal is accurate, certain, and direct.
Roadmap exercises should focus on identifying gaps in data architecture, technology and tools, processes, and skill sets. A gap analysis may prompt a review of the use cases prioritized in Step 3. Again, business stakeholders play a key role in prioritizing these initiatives based on complexity, budget, and costs and benefits.
How to Confidently Adopt a Big Data Strategy
There is no effective strategy without a plan to make it practical across the organization. Therefore, it is important to consider:
- Identify infrastructure challenges. Effectively leveraging data, especially data residing in legacy databases and outdated systems, can require changes to IT infrastructure that are incompatible with big data technologies. Identify areas that require infrastructure changes and involve stakeholders to ensure that various departments and users do not lose access to critical data.
- Evaluate employee resources. Having a killer strategy is one thing, but without the right role and the right skill set, that strategy is useless. Don’t underestimate the HR department in your big data strategy. Big data teams have the necessary skill sets to make sense of the data and need to translate those findings to various business unit leaders. Without a great team, the entire vision can be compromised. HR plays a key role in finding and hiring the talent you need. Also, don’t underestimate the capabilities of your current workforce. In some cases, a small amount of reskilling or upskilling can transform your current workforce into the critical roles needed to execute your strategy.
- be agile. You should try to build flexibility into your roadmap. You need to be able to quickly and easily adjust budgets, employees, use cases, and priorities based on changing conditions and the insights you gather.
Take the next step in your enterprise big data plan
Staying agile is the most important principle when implementing a big data strategy. Big data strategy he is not a one-off task, because data sources and big data technology are not static. But our continued dedication to thinking strategically about data pays off. A well-thought-out, well-executed and flexible plan can help companies gain valuable business intelligence, make better business decisions, and reshape their business strategy.
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