Strategic thinking and planning are crucial to growing a business.

For the past few years, businesses have increasingly turned towards data analytics to assist in decision-making.

Entrepreneurs and enterprise leaders should take advantage of the shift from traditional methods to more modern ones, by providing their analysts with the proper tools to uncover insights and design an up-to-date process that drives the business forward.

Strategic thinking and planning are crucial to growing a business.

For the past few years, businesses have increasingly turned towards data analytics to assist in decision-making.

Entrepreneurs and enterprise leaders should take advantage of the shift from traditional methods to more modern ones, by providing their analysts with the proper tools to uncover insights and design an up-to-date process that drives the business forward.

The essential do's and don’t’s of data analytics

-  don't accumulate data only for the sake of collecting it; it's important not to waste resources collecting a huge amount of data if there's no clear plan or strategy in place.

-  it's vital to know why you're collecting data and how useful is it going to be for your business; this is because any data that you obtain becomes meaningless without a proper action plan ;

-  create a comprehensive plan for your business, taking into account your marketing objectives, and synchronize the plans with your team.

-  tools and strategy for data analysis; it's important that you first identify your goals and having that as the foundation, you can determine the data you need to collect and analyze.

-  cultivate a culture in your organization that's data-driven and make sure that all data is properly analyzed, understood, and implemented in the decision-making process.

Good data strategy is the key to creating impactful change in your organization; don’t introduce data without context; the biggest pitfall to avoid is the assumption that your audience has the background knowledge to understand the reports you present to them.

It's imperative that you relate your report to what's relevant to all the stakeholders such as long-term goals, KPIs, market contingencies, hypotheses, etc.

Otherwise, the data will become meaningless.

Do tell a story with your data; the easiest way to make your data relevant is by telling a story, present the information obtained in an incisive manner, and a clear and captivating format that provides all necessary additional information and offers conclusions and insights with context.

In other words, instead of showing facts and figures, turn them into a meaningful story.

Don't lose focus with all sorts of data available today; it's not surprising that you can easily lose sight of the big picture.

The more accessible metrics you have in hand, the easier it is to get lost in the data.

What's needed is to take some time and regularly consult the stakeholders and decision-makers to filter out what's needed and what's not.

In general, because in today's time everything is as easily available and accessible, we

Last but not least, identify and focus on specific data that you should be analyzing.

The most efficient way to stay on target is to always focus on data that can lead you to the most relevant and actionable insights.

Stick with the best practices, avoid the don'ts, and data analytics will provide you with complete transparency over your business and actionable data.

 With an effective data strategy, your company will be able to quickly identify weaknesses and incompetencies, making more efficient and realistic plans, and also minimize the effort required to evaluate complex interrelationships.

Strategic planning driven by data will help you completely transform your business.

The next step for your business is to be updated with the current innovations that can elevate your business above your competitors.

Growing a Data Analytics team takes time and requires plenty of effort, investment in tools, and continuous training. Hiring an external data analytics team can help your business.

The Six Pillars of a Data Strategy

The first step in delivering transformational change through data is creating a Data Strategy.

Hold on! 

Interestingly, Data Strategy isn't just about data.

Now, you might think of things like data management, and data governance, and moving data around - which are really important components - but actually, a Data Strategy for us is about a framework that allows you to generate business value through the application of data and analytics.

• First up is Vision and Value.

The Vision and Value pillar of the Data Strategy is all about aligning your data initiatives, and programs, and work, and efforts, around a business outcome that aligns itself to the business strategy.

• Next up is all about People and Skills,

Regardless of the size of your organization, there are a set of capabilities that are needed, in order to technically sort the data, and from a business perspective, get the most value out of it, and ensure that you drive business change.

Now, here we need to think about who is going to take ultimate responsibility for the Data Strategy within your organization - lots of people are opting for a Chief Data Officer in this space, and that Chief Data Officer needs to put a team made up of business engagement, solution delivery, and governance, to ensure that you can maximize those opportunities that you want to go after.

The third pillar is all about how you organize those people and skills in the right way that will maximize those opportunities that you're trying to go after.

And this is about:

1. do you put those people centrally in one team?
2. do you distribute those people across your organization?
3. or, do you have some sort of hybrid, where you have central capabilities and best practices, but local subject matter expertise.

In this pillar, we also need to think about the Operating Model, and this is how you will work - this is the methodologies that you will use, this is the thinking that needs to go into how you will operate, in order to be agile, responsive, proactive, and iterative, and innovative, with your data efforts.

• In the fourth pillar, we finally get to talk about data, and this is Data Governance.

Data Governance is all about, how do you drive trust in the data, in the numbers, in the insight, in the output that algorithms give, so that people believe in what they're looking at, and believe what they're seeing.

• The fifth pillar is about Technology and Architecture.

Technology is about choosing the right tools, and architecture is about designing how you knit those tools together, and the architecture that you put, and the applications that you build in those technologies.

• You need to think about how you get data into your organization.
• You need to think about how you manage, and govern, and look after that data.
• You need to think about how you visualize and get insight, from that data.
• You need to think about how you build models, algorithms, machine learning engines, that deliver real high-value outputs for you.

And, you need to think about, how do we provision and push that data, potentially to other systems, to other companies, to other parts of the business, so that they can receive that data and do something with it in their own right.

• The final pillar is the Roadmap, and the Roadmap is about laying out a plan for all the other pillars that we've talked about in a time horizon so that we have a framework that we can manage, facilitate, and communicate how we're going to make all of this business change.

This in its own right becomes a really fundamental tool to help explain to the organization, and help you judge yourself, and test yourself, and score yourself, about how you're performing, in terms of implementing and executing your Data Strategy.

So that's it - the Six Pillars of a Good Data Strategy, that provides you with a framework that helps you articulate how you'll deliver business value, through the application of data and analytics.

How ZARA uses Data Analytics to run a profitable business

Clothing brand Zara’s replenishment cycle model is one of the keys to its success.

While Zara sells over 11,000 designs each year, their direct competitors only sell 2,000 to 4,000.

The key to this huge difference is Big Data and Analytics.

Zara produces over 84 crore garments each year.

This is divided into two parts:

-  15-25% is produced before each “season”.

-  the rest is produced after intense data analysis.

This results in very low inventory levels.

• Here’s how it works:

Every product has a unique RFID tag. The company tracks, analyses, and calculates its sales through this tag.

Zara’s designers also get feedback through their big data systems about public opinion.

Based on this data, and the quick replenishment cycle model, Zara has better inventory management, design, and distribution.

Any business, whether a small business or mid-size can benefit from analyzing data.

Dokalink’s selection of top Data Analytics provides over 100 Top Data Analytics Companies that can help you analyze your core business, providing you the information you need to optimize and grow your business.