Data Strategy For Your Business

In a competitive industry, a strong marketing strategy is essential for standing out from the crowd. It is no longer enough to simply have the best product or service on the market; you must also reach and persuade the right customer.

There’s a good chance your competition understands the value of strong marketing and has at least a basic strategy in place to catch your audience’s attention. For many businesses, basic marketing techniques are no longer sufficient.

Whether you’re developing your own brand’s strategy or working with multiple clients to do so, there’s one essential truth that all marketing professionals understand

Understanding your audience is the most effective way to achieve success.

You have a better chance of success if you can tailor the perfect marketing strategy to meet your goals with your intended audience, informed by their preferences and interests. This is where data marketing comes in.

As you will see, data marketing provides specificity and efficiency to your strategy that you would not have otherwise. You can determine and prove which campaign strategies will work best with your target audience using data.

Things to Consider When Creating an Enterprise Data Strategy : 

1. Understand your company’s goals

Link your data strategy to your business strategy.

Buy-in is essential for any good data strategy. Your data strategy structure will only work if the vision is controlled, supported, and monetized in conjunction with the organization’s overall goals.

you must first understand the organization’s goals and senior leadership to align business and data properties. Meeting with the C-suite and key business stakeholders is the first step toward assisting your organization in achieving its goals by integrating data as a true competitive advantage.

As your collaborative, data-driven environment begins to take shape, make sure priorities are clarified and agreed upon to help leadership see the strategic value of data.

2. Assess your current state

The first step is to conduct a self-assessment before developing your strategy. Consider where your company stands in terms of data maturity.

Dell has a broadly used “Data Maturity Model” that assists businesses in determining how data-driven their organization is. There are 3 phases to consider:

Data aware – Your company’s reporting system has not been standardized, and there is no interconnection between your systems, data sources, and databases. Even  there is a lack of confidence in the data itself.

Data proficient – There is still a lack of trust in data, particularly its quality. You might have invested in a data store, although some pieces are still missing.

Data-driven – IT and business collaborate closely and are on the same page. Because the foundation work has already been completed successfully, the focus is now on scaling the data strategy. What’s most essential here is to be honest about where your company stands.

It’s not enough to consider your feelings about how data-driven your company is. Consider the facts. Begin by identifying the current data issues that your company is experiencing, as this is a great indicator of where you stand.

3. Create framework for your data strategy 

Define the desired state of your data

Your target state, operating model, and implementation blueprint will assist you in developing, improving, and evolving your data strategy, as well as empowering your teams to navigate challenges using a consistent data management strategy. Outline your comprehensive vision so that data strategy discussions and the resulting business process changes are as important to app engineers and business analysts as they are to HR and sales.

Digital necessitates real-time decisioning capabilities, and the predictive models that enable these capabilities necessitate data science environments. Operational data is becoming an increasingly important component of your data ecosystem. To ensure consistent data quality, a modern data architecture necessitates an integrated data ecosystem with capabilities that must be managed, governed, and secured.

This level of detail makes changing business processes easier because you’re prepared to meet data concerns with a detailed explanation of how this will make a specific user’s life easier. According to a recent survey, 37% of respondents ranked data security as their top challenge, followed by data privacy concerns and managing data pipelines.

Be specific about application modernization, automation, and artificial intelligence

The more you learn from your digital transformation and information technology strategy, the better your data strategy will be. Such insights can help drive efficiency, increase revenue growth, and reduce risk, especially when combined with app modernization, automation, and AI.

Capture and share the highlights of your data strategy.

You should be clear on your organization’s priorities and how to use data and AI to deliver and accelerate business value at this point. What are the next gaps you need to fill? A look at the big picture—where you are now and where you are going provides strategic context for making actionable plans for delivery and scale. Include the outcomes, objectives, and measures that will keep you on track so you can share them with your organization as the journey progresses. Here are some examples of what you should include in your data strategy overview:

  • Goals, outcomes, and metrics
  • Data privacy and security requirements
  • Supporting technology and reference architecture
  • Observations, challenges, and suggestions
  • Cross-functional data must support a variety of use cases.
  • Pipelines, data topology, and data organizations

4. Metrics for measuring success

Discover why ongoing analysis is critical for data marketing.

Effective data marketing does not end with the end of your campaign. The whole point of these

strategy is to use data to improve your marketing efforts. To continue doing so, you must have a  strategy in place for analyzing the success of your campaign.

Take note of those who responded to your campaign and those who did not. Were there any unexpected results, such as likely respondents ignoring the effort or unlikely respondents acting in the opposite manner?

Investigate why they responded further. Is there anything in common between your non-responders or non-buyers that correlates with their response behavior?

Use this data to guide your future marketing efforts. With this predictive analysis, you can identify buyer personas and broaden the prospects of your future campaigns.

Forecasting modeling can help you achieve your goals. Data marketing relies heavily on predictive modeling. 

Conclusion


Vispan Solutions is passionate about data and its potential to improve business. Let us begin with data that will make a difference.