10 Best Practices For A Successful Data Strategy

Data is an effective resource that needs to be used and taken into account. By creating and putting into practice a data strategy, any firm can transform data into an enterprise asset. A very dynamic procedure used to gather, arrange, evaluate, and distribute data in support of corporate goals is called a data strategy.

A data strategy is essentially a set of rules and guidelines outlining the proper way for your company to handle its data. It lists the accountable parties, the procedures to be followed, and the equipment to be used.

With a data strategy, organizations can coordinate their activities and prevent resource waste. Even though a data strategy may not always be ideal, it must have the ability to guide the organization's solutions when necessary. This is crucial because of how quickly enterprises are evolving in this constantly changing global landscape.

How is a data strategy implemented?

Policies and processes that support the organization's long-term objectives for data use make up an organization's data strategy. For your plan to be effective, it must address every usage of data. It also needs to cover things beyond technical data management and analysis processes. In data management and interpretation, as we shall see, the human element is equally significant.

Why is a data strategy necessary for your business?

Any modern company plan requires data. We can no longer leave the administration, protection, and use of such a vital company asset in the hands of lone data architects or developers. To ensure that data is handled and used properly, it would be ideal if you had a thorough data strategy with broad support and participation. Every organization has different priorities for different types of data according to its business goals and management approaches.

10 Best Practices for Data Strategy

1. Motivate people to think data-driven

Being data-driven is about having an established urge to look for answers and base decisions on data, not on technical skill or mathematical competence. The first thought should always be to look to the data for insights when concerns about competitor movements, acquisition peaks, or client turnover arise. Developing this data-driven culture within your team means establishing the practice of providing hard evidence to back up decisions. Encourage them to approach any issue or novel concept by first asking, "What do the numbers say?" This change in viewpoint will enable your team to decide with evidence-based wisdom, resulting in a more calculated and effective strategy.

2. Make data an organization-wide priority

A well-received data strategy is one in which all parties participate. Individuals must be aware of it and open to implementing it.

In this situation, data must be viewed as a strategic asset and a company value. How would one go about doing that?

• By fostering a culture that prioritizes data. To do this, there are several processes involved.
• Appointing an executive leader or "champion" to promote the data strategy and associated projects.
• Gaining support from managers at all levels, starting with team leads in the C-suite. All parties involved must prioritize and support a data strategy.
• Establishing uniform definitions, guidelines, and measurements for the entire organization and, if required, setting up internal training
• Putting in place transversal, cross-functional data processes using a single source of truth
• Democratizing data by enabling non-tech teams to access it (e.g., by purchasing a no-code data extraction tool for marketing and sales)

3. Remain straightforward

Data being sent to investors, the board, or the boss? Just focus on the one or two numbers that would most likely affect the choice they make; they don't need to know how hard you worked. This doesn't imply that you don't put in the work. Keep something hidden away so you can respond to inquiries. The Very Important Person only needs to view the spreadsheet's bottom number, which indicates what you advise them to do.)

4. Monitor KPIs and analytics

In a data-driven organization, measurement is everything. It is crucial to match your strategic goals with Key Performance Indicators (KPIs) to accomplish this. Measuring progress and identifying opportunities for improvement requires tracking meaningful indicators, not vanity measures. The three main KPIs for SaaS organizations are frequently LTV (Lifetime Value), Churn, and MRR (Monthly Recurring Revenue). While KPIs are typically assigned to individuals and teams, important projects and initiatives should also have measurable targets set for them. By using the OKR (Objectives & Key Results) framework, you can successfully combine KPIs with long-term objectives, encouraging you to investigate the "why" behind the figures and guaranteeing that data and strategic ambitions are in line.

Data quality metrics are also possible to have. Monitoring data quality allows you to:

• Analyze how successful your data strategy is.
• Recognize the accuracy of your data.
• Emphasize data that is inconsistent, partial, or missing.
• Take remedial measures to enhance the quality of the data.

5. Don't follow the newest technology trends

Technologies that support data management best practices are highly touted. That being said, you don't have to follow every new fad. You most likely don't require AI or big data in practice. Not many businesses use AI or big data at all. Our understanding of what constitutes a strong data strategy is obscured by trends and catchphrases. A basic tech stack is not as successful as a data strategy. Success is more about the process and the quality of the data than it is about technology.

Use one of our business clients as an illustration. To assess their US market share, they sought to obtain market data. Even though it was limited to pulling from 20 websites, it was sufficient to run its internal analytics engine without the need for more sophisticated hardware.

The metadata, or description of the data, is even more significant than the technology or amount of data. Metadata is necessary to interpret unprocessed data. Therefore, disregard the trends and concentrate on the metadata and processes.

6. Choose the best individuals to lead the data strategy

Technical knowledge isn't a must for your data strategy lead, but it is if you want to thrive in cross-functional collaboration, make data-driven decisions, and comprehend how data relates to business objectives. For this position, think of a product manager who sits at the nexus of technology, sales, and marketing.

The roles of other stakeholders must be well-defined. This could involve data engineers, scientists, analysts, and business managers, depending on the scale of your firm. The framework suggests departmental data stewards, an audit procedure, and data governance principles. Tools and data analysis will probably need to be expanded to implement a data strategy. Assess the current skill set of your staff and, if necessary, hire more analysts or provide internal training to fill up any gaps.

7. Create robust data security procedures

Everyone is concerned about data security. Dealing with it is not limited to your external data protection officer, IT department, or legal department. You will never be in charge of data security if it is not in your design. A data protection law for citizens of Europe, the General Data Protection Regulation (GDPR), mandates that enterprises disclose information regarding data storage, access controls, and use.

You should be more cautious about who has access to and how the data is dispersed the more data that is shared between teams. Data should only be accessible to those who require it. Developers don't necessarily require access to comprehensive client data or sales statistics, for instance.

When structuring the way that data is shared between teams and systems, data security must be given high priority out of consideration for customers.

8. Choose the appropriate methods and tools for data analysis

Everyone should be empowered to access and draw insights from relevant datasets, whether it's a sales representative exploring lead data or a product manager analyzing user behavior for purchase signals. However, successful data analysis requires the right tools, methods, and training. Predictive analytics, text analysis, cohort analysis, cluster analysis, and sentiment analysis are just a few examples of the diverse techniques available. Choosing the most effective methods involves balancing strategic insights with adherence to data governance and privacy regulations. Handle use cases involving personally identifiable information with caution, as not all analytics methods are legally permissible. Ensure your internal teams are aware of these limitations to foster responsible and compliant data utilization.

9. Create a Singular Source of Reality

Data's provenance frequently gets twisted and challenging to follow when it moves across teams and systems, raising questions about its correctness and quality. A Single Source of Truth (SSOT) becomes essential in this situation. Critical business choices are no longer at risk of being guided by untrustworthy or isolated data since the SSOT offers a central store of consistent and trustworthy data that is publicly available. Upholding strict data quality standards via an SSOT is essential since inaccurate data can result in expensive errors. Developing a data pipeline that takes raw data from multiple sources and converts it into a standard format for analysis and storage is a popular method for accomplishing this. Furthermore, to guarantee transparency and accountability throughout the data's lifecycle, your data governance process should carefully record the data's source and any transformations made to it.

10. Enhance results by automating data

The process of obtaining, manipulating, and storing data using automated methods (instead of by hand) is known as data automation. For teams who depend on obtaining web data, like sales representatives exploring leads and possibilities, data automation is a big time-saver.

• Utilizing data automation can help
• Make a list of qualified target accounts.
• Create an automated lead generation system.
• Add new information to a CRM or score leads
• Determine purchasing indications and market patterns.

Use a data automation solution that extracts, converts, and loads data into your CRM for analysis to automate data sourcing. Typically, web scraping powers these kinds of instruments. It is advisable to stay away from APIs, scripts, and complex technology when automating data for non-technical personnel. Instead, make use of a no-code automation tool such as Piloterr.

Keep Evaluating and Developing Your Data Strategy

A successful data strategy is a dynamic process that requires ongoing observation and improvement. It is not static.

How to do it is as follows:

1. Analyze Data Performance Often

• To evaluate the success of your data strategy, monitor important metrics such as KPIs and data quality indicators.
• Determine what needs to be improved, such as the accessibility, correctness, and completeness of the data.

2. Adapt to Changing Business Requirements

• Consistently assess your objectives and make sure your data strategy is in line with them.Be prepared to modify your plan as your company grows and demands change.

3. Embrace new technology

• Keep abreast of the most recent developments in data technology and tools, and think about incorporating them into your plan.
• Utilize AI and machine learning to streamline processes and extract more meaningful information from your data.

4. Promote Data Literacy

• Invest in educational initiatives to enhance the data literacy abilities of your staff.
• Promote departmental cooperation and knowledge exchange to develop a data-driven culture.

5. Conduct Regular Reviews

• To assess your data strategy's efficacy and pinpoint areas for development, plan recurring reviews.
• Get input from all relevant parties within the company to obtain insightful information.

You can make sure that your data strategy stays current, efficient, and in line with your changing business requirements by regularly reviewing and enhancing it. By taking a proactive stance, you may optimize the worth of your data and achieve superior business results.