Data is the centerpiece of any industry. Today everything is about data. Organizations are dealing with big data, both structured and unstructured. Gartner estimates that only 18% of organizations take advantage of unstructured data, and 82% of organizations find it impossible to manage and get the value out of unstructured data. Organizations believe unstructured data has no value to add, but it is a hidden gem that can benefit you more when you approach it appropriately. Organizations should focus on both structured and unstructured data to get driving insights. Managing big data comes with a lot of challenges and setbacks. So, You need proper planning and a strategic approach to master your data.
Data handling goes beyond collecting and storing data but bringing the value out of the unstructured can help stakeholders make faster data-driven decisions and improve business performance. Data gives you a competitive edge and help you create a unique differentiation among your peers. To sum it up, managing your organizational data wealth is a critical aspect that cannot be left ignored.
Mastering your data strategy is no easy task. So we have curated some of the significant aspects any organization must consider while formulating its data plan.
Table of Contents
Customize a data strategy
Before diving into all the new technologies and tools for managing your data, you need a data strategy. A data strategy helps you to set a strong foundation for your data process. It ensures the data remains manageable and provides valuable insights for effective decision-making. Organizations should prioritize their key objectives and expected outcomes while chalking down a strategy plan, as it helps you stay aligned and avoids any deviation.
Below are the points to consider while designing a data strategy,
- Align your business vision with your data strategy
- What are the questions your data need to address?
- Does your historic data aid in answering the critical question and help achieve your goals?
- Will it help you in decision-making, trend analysis, pattern analysis, improving products or services, internal operations, and revenue?
Define a best fit Data Architecture
Often organizations get confused with data strategy and data architecture. While data strategy is where you set your data objectives and align your business goals with the data objectives, data architecture is the scheme supporting your data management journey.
Data strategy is the initial step that requires a strong backing of data architecture definition and implementation. It provides a blueprint for databases and applications. Data architecture helps you understand how to collect, integrate, organize, enhance, store, and get insights from your data. Well-designed data architecture will help you avoid the malfunctioning of the system.
Plan your Data Storage
With 80% of organizations on the cloud and more planning to migrate in 2022, cloud computing providers are ramping up their game, providing better scalability and visibility to your data landscape. Most public cloud platforms like Google Cloud, Microsoft Azure, and AWS provide pay-as-you-go features where organizations pay for the usage, avoiding unnecessary costs. Vendors offer plenty of features and intelligent tools to help organizations view and manage their data efficiently. There are different tiers and classes you can leverage to place your data. Most organizations find multi-cloud and hybrid clouds more adaptable, owing to their existing data reality.
Before opting for any cloud storage, organizations should check the policies and the data security features along with the compliance norms offered by the vendors. Avoid data breaches and data leakage and map compliances with your company’s policies.
With the data proliferation across all businesses, there is a need for scalable storage comparatively. Scalable and flexible storage options help reduce IT and operational costs and provide high security and disaster recovery. Furthermore, automating operational processes with AI and ML helps reduce data engineers’ effort and time.
Streamline your Data governance
Organizations are trying to avoid data silos. Organizations with proper data governance enjoy the benefits of improved data quality and security, lowered management costs, and balanced data collection and policies.
Why is Data Governance so important?
Data governance helps organizations make data accessible across teams reducing dependencies and time. Automating the process with ML combined with SQL on real-time data sends the required data to the sales and marketing teams to initiate a marketing campaign avoiding data silos.
Manage your Data Quality
The quality of the data directly impacts the quality of your decisions. Companies collect an enormous volume of data but use only a part of it to get insights. Poor data quality costs up to $3.1 trillion yearly to the US economy. The data quality directly impacts the decisions you make. The quality of the data is determined by how well it serves its purpose, depending on factors like accuracy, completeness, relevance, timeliness, and consistency. Organizations should set data quality parameters and a threshold to calculate data quality scores. By doing so, they can have visibility of their data quality. When organizations extract insights from poor-quality data, it can cause a disaster.
Insights from data
According to the Forrester report, 74% of organizations want to be data driven. To be data-driven, you need actionable insights. Actionable insights help you make decisions with promising results. Insights are where all the structured and unstructured data take their form and show trends and patterns.
One of the significant characteristics of insights should be clarity with visualizations that are easy to understand. If the stakeholders or the businesspeople find it hard to understand the insights from the data, then it has not served its purpose. Organizations should make insights that are clear, specific, relevant, and aligned with their purpose, to get the most out of their data.
