Why Decision Makers Must Prioritize Data Analytics Strategy In 2023?

5 mins

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Darshan Shah

December 12, 2022

Imagine you have a task of finishing the ad campaign in the next two days, and your 2 member team finds it difficult to row against the current. And luckily, your team gains a new member. This new member jumps straight away and starts the work on the ad campaign - but in the other way. Not aligned with the whole team!

This can be a disaster, right? Not only are their efforts useless, but they are also slowing down the entire ad campaign. This newcomer is leading to slowing down and undoing some of the work the rest of the team has already completed.

This is precisely what happens when your organization's data and analytics (D&A) activities aren't aligned with its business objectives. The large-scale actions and investments not only fail but also kill your company's momentum.

Many businesses enter the D&A market without having a data strategy. The outcomes can be fatal to the organization. Here is the ultimate guide to designing a data analytics strategy for your business.

Importance of Data Analytics Strategy

Business leaders collect, store, and analyze operational data from various sources, including digital media, social network data, online transactions, financial analysis, and genomics research. The most precious asset for any corporation is organizational data.

An efficient data strategy can enable an organization to gain insight from any data under its control or influence, whether organized or unstructured, in motion or at rest, regardless of the specific constraints and demands of the business.

A strategy solely focused on current objectives may eventually impede growth due to expanding data sources and a rising need for data-driven insights. By incorporating flexibility into your data analytics strategy, your business units can respond to new demands as they arise.

A data analytics strategy should help organizations in the following areas:

An effective data strategy assists every business unit in identifying the core issues that need to be addressed through an ROI-driven strategy.
A data analytics strategy formulation draws together internal and external data sources to dissolve data silos. This offers the most business value based on its capacity to carry out.
Even a fundamental data strategy can help a company thoroughly comprehend data across the entire business value chain and use analytics to derive important business insights.
IT directors can turn raw data into value with a sound operational model and map high-value use cases to critical business goals.
Data can be made available to everyone through self-service technologies by investing in building a data analytics strategy.
A data strategy-enabled digital feedback loop may assist business units in transforming employee, product, and customer data into intelligence and enabling altered experiences.

However, putting data at the company's center poses difficulties, so you should begin by determining whether you have the essential components of a data analytics strategy.

7 Elements of a Successful data analytics strategy

Data-driven decision-making improves business outcomes. What information you obtain and how you analyze it will depend entirely on what you're trying to do, so you need to consider this from the beginning.

A robust data strategy makes everything go more smoothly and prepares you and your cross-functional team for the voyage. What, therefore, ought to be part of your data analytics strategy? Let's examine the subsequent seven areas:

1. Business Strategy

Your organization's overall business models must be influenced by a sound corporate strategy for data, including its strategic priorities and fundamental business questions. Then you can decide how to use data to support those priorities and provide the solutions to your operational queries.

2. Immediate Priorities

Implementing and providing value from strategic data analysis priorities can take some time. Decide on 1-3 modest achievements for data strategies. These are quick and reasonable methods for you to provide value and show a return on implementing data strategies. To help avoid or reduce customer turnover, conduct a customer churn study.

3. Data Requirements

Establish the data you need to fulfill your goals and where you'll get it. This comprises:

Do you require structured data, unstructured data, or, ideally, both types of data?
Can you accomplish your goal using only enterprise data, or do you also require external data (such as data from social media, weather, etc.) to complete the picture?
Do you already have the necessary information, or can you easily get it?
If not, you must create a system for gathering the necessary information. What technique will you choose for collecting data?

4. Data Governance

If you don't pay close attention to data governance, data can become a major problem in addition to offering great rewards. Consider data management, security, privacy, ownership, openness, and ethical data use. Important factors include:

Who ensures the data is true, full, and current?
How will we guarantee data privacy?
Could you lose access to data you're using that belongs to someone else? If they ignore you, what will you do?
How can the moral use of data be ensured?
How can you ensure data security with a good data strategy?
Is your data management team well-equipped?
What authorizations are required for you to collect and utilize the data?
How can data be reduced to the absolute minimum?
How will you ensure that data is used honestly and openly throughout the company?

5. Technology Implications

The following stage is finding your decisions' technical and infrastructure consequences after deciding how and what data you'll need to employ. To put it simply, this entails assessing your hardware and software requirements for:

Data collection
Data access
Organizing and storing data
Processing data, which may use data platforms, to derive insights
Using reporting and data visualization to share data insights

6. Skills and Capabilities

The greatest barrier for businesses trying to make better use of data is frequently a need for more data expertise and knowledge. Chief data officer, data scientists, data analysts, and similar roles must make relevant contributions by creating a good data ecosystem within the organization. As a result, this is a vital need for your data analytics strategy. Think about it:

Do you possess the expertise to meet your customers' data needs?
Does your organization need to train internal employees?
Would new talent need to be hired?
Will you collaborate with a data provider if you're considering hiring people with outside talents?
Is there room to buy a business?

This also includes the importance of leadership awareness. Your leadership group must comprehend the value of data and how it may advance the company's goals.

Ideally, this data culture will spread throughout the entire organization, making everyone at every level conscious of the power of data.

7. Data Deployment and Change Management Concerns

Planning is one thing; carrying it out is another. So, this final stage aims to ensure that your data analytics strategy is implemented. This comprises:

How are you going to carry out your plan?
What tasks must be completed immediately?
Who is in charge of carrying out each action?
What adjustments should the company make?

After thoroughly examining each of these topics, you can begin to formulate a more formal data analytics strategy.

5-Step Guide to Develop an effective data analytics strategy

Getting the main decision-makers and players in the firm involved can help you develop a stronger data analytics strategy overall. Getting their support at this vital early stage increases the likelihood that they will use all that data effectively in the future.

With an architectural and structural design, simply investing in data technologies and tools can allow an organization to save time, money, and resources. Here are five stages to help you develop a data analytics strategy for your company.

Step 1: Determine Your Business Goals

It is much preferable to start with company objectives rather than the data itself (i.e., what you already have, what you might be able to access, or what you want to have). Why, after all, bother gathering information if it won't assist you in achieving your corporate objectives?

Consider the tactical goals you've set for the upcoming months or years. Decide what to accomplish, and then consider the significant open-ended questions you must address to carry out that strategy.

In short, determine your goals and use data to help you get there:

Are you trying to expand your customer base?
Do you wish to comprehend your present ones more fully?
Want to know where the ideal places are to offer your service?

Step 2: Identify the Ideal Data

It is even more crucial to think small in this era of data. Create a small database that aids the business in resolving its most pressing issues instead of a large one. This is a fantastic way to view the data.

Considering this, go through the following steps to recognize relevant data:

Take a look at each of the questions you listed.
Consider the perfect information you would require or want to answer that question.
To find out what information you already have, look inside your enterprise data strategy highlights.
Determine what information you might have access to by looking outside.

But remember that you will only know where to look for data or how to collect data if you know what data sets you need.

Step 3: Define Your Analytics Requirements

Businesses continue to demonstrate a strong interest in this area, with 48% of Chief Information Officers saying that their enterprises have already utilized or plan to deploy AI and machine learning technologies.

Once you're clear on your information needs and the data that must be collected, you need to specify your analytics needs or how you'll analyze data to provide insights that will help you respond to your inquiries and accomplish your business objectives.

Traditional data collecting and analysis, such as point-of-sale transactions, website hits, etc., is one thing. Still, unstructured data, such as email chats, social media posts, video content, and so on, hold much of the potential of data.

Much of the value is found when combining this chaotic and complicated data with other, more conventional data, like transactions, but you must have a plan for the research.

Step 4: Identify Data Visualization Techniques

Data is only meaningful if the most important findings from it are effectively communicated to the appropriate audiences to aid in decision-making.

Your data will be more effectively utilized if you use data visualization tools appropriately and take the time to highlight and present important information user-friendly.

The most crucial thing to remember at this point is your target audience. Therefore, you must specify in this stage how the insights will be shared with the information consumer or decision maker.

Consider the ideal format and how to make the insights as visually appealing as you can. You should also consider if interactivity is necessary, i.e., do the important decision-makers in your company require access to interactive self-service reports and dashboards?

You need to specify the hardware and software requirements based on how the data will be delivered to the end user. Consider the following:

Does your present data storage system work properly?
Should cloud-based solutions be added to it?
What reporting and analytics capabilities do you currently have?
What do you require?

Step 5: Determine the Plan of Action

You are now prepared to design an action plan to translate your data analytics strategy into reality after identifying the numerous demands mentioned above. This action plan will include important milestones, participants, and duties like any other action plan.

Make a strong data strategy framework for data to the people in your organization after developing your data analytics strategy. This will persuade them of the value of using data and connect the advantages to business Key Performance Indicators (KPIs).

You must recognize the company's training and development needs and any areas where you want outside assistance for making better business decisions.

Remember that, like with any business processes for improvement, things could change or advance along the route. Your data may point to intriguing new questions you wish to investigate or prompt changes to your current data analytics strategy. Review your data approach and reevaluate each item mentioned above if that occurs.

Get Started with Your Data Analytics Strategy

To fully utilize data and analytics and achieve operational efficiency, businesses must locate, combine, and manage many data sources. They also require the capacity to create advanced analytics models for forecasting and enhancing results. The ability of management to change the organization such that the data and models result in better decisions is of utmost importance.

Further read: 7 Attributes Of a Highly Data-Driven Organization

Why Decision Makers Must Prioritize Data Analytics Strategy In 2023?

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