Establishing and running a business demands many things and a viable data strategy tops the chart. This is no longer a hidden fact that quality data and its effective utilization is what places a business way ahead in the competition. The fate of data is decided by implemented data strategy. The efficacious strategy is, lucrative are the outcomes.
Starting from improving the data quality to providing deeper insights to the organization, a smartly designed data strategy can do wonders. The post educates businesses and enterprises about the significance of data strategy, its key components, challenges, and viable way to fix them.
Also Read: SAP Master Data Governance – Streamlining Business Processes
Data Strategy – Meaning and Significance
By definition, a data strategy is a plan designed to manage the business data in a way that it brings out the maximum outcomes and support business growth. By making most of the offered data, data strategy empower businesses to be future-ready and churn out better ROI. Implementation of data strategy allows businesses to:
- Define and explain the utility of data in a given scenario;
- Decide how and where data should be used;
- Depict the changes that organization must make in the operation to drive maximum value from the data activities;
- Create a timeline for every data utilization activity;
- Define the financial outcome for the data set and create plans to turn them into reality.
Why Data Strategy Is Important?
Data strategy can be called a pillar of success for the organization, but why? Scroll down to figure out this.
- Allowing unleashing the full power of data
Today, data is the gold for businesses, provided it’s interrupted and consumed the right way. This can only be done with the help of a data strategy. A well-designed data strategy can answer questions like ‘What is big data, ‘Where should this data be used’, ‘what to expect from certain data-set, and ‘how employees can make most of the offered data’.
Download Guide: Open Data Initiative
Having these questions resolved allows organizations can easily explore the full power of data and utilize it for maximum fruition.
- Zero data wastage
Data volume is increasing with each passing day and organizations are having a tough time managing this asset. The expanded data volume tends to do cause huge data wastage, which no organization can offer. With data strategy, one can juice the data up to a maximum extent and reduce the data wastage.
- Better data management in the least possible efforts
Data management is a very tedious job. Despite that, one can’t turn a blind eye towards this action as data, with is not effectively managed, is not good for the organization’s well-being.
Data-related issues should be fixed in the infancy stage and organizations must define data access and usage criteria. Generating a company-wide data strategy addresses all these data issues effectively and allows each department to work towards the same cause.
- Proficient use of resources
The absence of a data strategy will force different departments to use data as per their needs. This separate data handling is time-consuming. Data strategy offers a standard data utilization format across the organization. With this pre-defined data usage strategy, organizations are allowed to speed up the data utilization process while keeping resource wastage as little as possible.
Key Components of Viable Data Strategy
Now that you’re aware of the utility of data strategy and the wonders it can do to an organization, it’s time to get familiar with the elements shaping a high-end data strategy.
Component #1 – Data
Of course, data is the prime part of a data strategy. A strategy should feature the strategic value of data, validating its quality, integrating processes, and policies governing its key usage. Additionally, enterprise data catalog should be a part of data strategy as this component allows organizations to make data available for usage and explain to the team where data resides.
Component #2 – Data tools
Data analysis is not something that humans can handle alone. They need the help of high-end tools to automate the menial jobs and expect the best-possible accuracy. A doable data strategy should feature key data tools to use and, if possible, their utility process.
Data tools are of all kinds and types. Some are easy while few are complex. One must make a choice as per the need of the hour. Make sure that data visualization, dashboard, and reporting tools are included in the data strategy.
Component #3 – Analytics techniques
Just like data tools, there are ample analytics techniques offered. For instance, there are data visualization, text analytics, predictive analytics, and so on. When demanded oversight is offered, analytics techniques provide the best value of a data strategy. Based on the team’s understanding and capacity, organizations must define analytics techniques in the data strategy.
Component #4 – Collaboration
To make sure the data strategy is implemented effectively, collaboration should be of a higher grade. Starting from data preparation to data governance, collaboration is essential. The use of collaborative tools makes discussion and debating easy and allows organizations to figure out the utility of the strategy.
Component #5 – Documentation and auditing
Data strategy should be well documented and audited to figure out what’s approved, what’s appropriate, what’s the purpose, and what’s the governance policy, and answers to many other questions. A good documentation and auditing technique allows providing a good explanation of data strategy and fundamental data architecture.
Component #6 – People
As your team and included people are going to use the data strategy, it should revolve around the people. Also, make sure that the right people are a part of it. For instance, data scientists, in-house or outsourced, should handle the data strategy.
Also, enough IT and data management resources should be in place. The right kind of people will make better availability, instant disaster recovery, and better adherence with service-level agreements possible.
Challenges to Face
Despite acknowledging the power of data strategy, many organizations don’t bring it into action. The reason being is its tedious nature. It takes a lot of design and implementation of a data strategy. Have a look at them.
- Businesses have a tough time figuring out the aim of data strategy. They know they have data and they know that they have certain issues to address. But, they don’t know how to make data work for those tasks. Defining the aim of a data strategy needs someone having deeper organizational and data-related understanding. Big enterprises can hire professionals for this job. The real hurdle is for start-ups and small businesses as the surged expenses involved in this task are beyond their capacity.
- In a team, it’s obvious to have different skills level and this dissimilarity is a major hindrance for organizations.
- Don’t consider your job done once you have a data strategy in place. One has to monitor its utility continuously. This adds an extra burden for organizations. Some even got tired of this ongoing task.
The Possible Solution
While one expects maximum outcome from data strategy, addressing these and many other hidden challenges is imperative. As not every organization can afford to have a dedicated team of Data developers or Data scientists, it’s wise to outsource data analysis services.
Success Case: Deploying Next-Gen Business Intelligence Solution For Telecommunications Industry
Many leading service providers, like Stridely Solutions, lend a helping hand to small businesses and start-ups in designing, implementing, and monitoring data strategies. Having such professional help ensure the best possible outcome from the at-work data strategy.
Ending Notes
Data, when used effectively, can change the fate of a struggling business. The practicable data usage can only be achieved from a diligently-designed data strategy. One must not shrug it aside just because it’s tedious and taxing. Taking the professional help of Stridely Solutions is the easiest way to have a worthwhile data strategy by your side while investing the least possible efforts.