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Dario RemigiSenior Research Analyst, BIBA Medical Ltd.

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Data Analytics Services and Solutions I Rishabh Software

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Rishabh Software is a data analytics services company offering cloud-based business intelligence, data visualization & data engineering services across industries.

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How Data Analytics is used to Improve Decision Making?

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Data is one of the most powerful tools available for orgnisations today and is considered a gold mine today. The proper usage of data can help you make strategic and critical business decisions that will immensely improve organizational performance.

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We've heard a lot in recent years about the revolutionary effects of and the internet of things ( IoT ). They have indeed had big effects in a number of areas, from retail to digital marketing and healthcare .

With so much data at our disposal, however, the difficulty is in knowing how to make use of it. This is where big data analytics comes in.

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Data analysis allows us to obtain valuable insights while saving time and effort. Without it, we'd be left searching for needles in the proverbial haystack of unstructured data .

What is data analytics?

In short, the term is used to denote the process of delving into raw data and drawing insights and conclusions from it.

Data analysts use analytics applications (with capabilities like machine learning and automation ) to help them gain useful insights. Open-source data analysis tools like Pandas -a tool built using the Python programming language -are increasingly commonly used.

An exploratory data analysis can provide an overview and summary of the main characteristics of datasets . Businesses can then use methods such as data visualization to present the insights they've uncovered to wider audiences-including customers and clients-and suggest next steps based on those insights.

Armed with the information gleaned from business analytics , firms are better able to meet the needs and desires of their customers (and to win new ones).

Types of data analytics

There are four basic types of data analytics , and these are as follows:

1. Descriptive analytics: Describes what has occurred over a certain period of time. For example, increases in sales or higher page views in a particular month or quarter.

2. Predictive analytics: Predicts what is likely to take place in the relatively near term based on previous data.

3. Diagnostic analytics: Analyzes why a particular phenomenon (such as an increase or decline in sales) has occurred.

4. Prescriptive analytics: Suggests a certain course of action (e.g. a seasonal increase in stocking of a certain product) based on past data.

There's also regression analysis; this term covers a number of statistical methods used to estimate relationships between a dependent variable and one or more independent variables. This can help to provide insights into the strength of the relationships between variables, and model their future relationships.

By analyzing historical data , we can make better business decisions as we're more able to anticipate fluctuations in consumer demand, and to understand why these occur. This, if used properly, should ultimately translate into better profitability and a healthier bottom line.

However, data quality and efficient data management matter a great deal. If your data are substandard or somehow unreliable, you won't be able to optimize your business decisions as you would otherwise. Structured data is indispensable.

Benefits of data analytics

Now that we've defined data analytics and outlined its different types, we need to look at the potential benefits it can offer businesses. There are numerous applications of data , so it's probably best if we concentrate on a few in particular here.

Some of the key benefits of data analytics include:

1. More precise marketing: With the help of data analytics , you can get to know your target audience better. You have a clearer appreciation of what they're looking for and what they need. This allows you to target them more precisely with better-crafted campaigns, including via social media .

2. Better decision making: Following on from our previous point, data analytics allows businesses to sharpen their decision-making skills. Equipped with a more thorough understanding of their customer base and their own performance, they can use the insights obtained via data analytics to make improved decisions as well as making project management more effective.

3. Enhanced efficiency: Thanks to data analytics , it's possible for businesses to streamline many of their processes, thus rendering them more efficient while also enabling them to cut costs. Thus, it also helps them with financial analysis , enabling them to deploy their resources more efficiently. For instance, with regard to targeted marketing campaigns.

4. Improved customer service: Last but by no means least, data analytics can also help businesses to improve their overall standard of customer service. Firstly, it provides in-depth insights into what customers want and their preferences. Secondly, storing data in a single central location and allowing your whole customer service team to access it can help to ensure better consistency of service quality.

How businesses can use data analytics

We've talked about how data professionals - data scientists and engineers-make use of data analytics , but it's worth stopping to discuss how businesses make practical, everyday use of it. This is a challenging undertaking because the volume of data involved is so substantial. It's important, therefore, to give careful thought to applications of data analytics .

1. Planning and strategy: Before doing anything else with regard to data analytics , businesses must ensure that they have a long-term plan and clear objectives in mind. They must ask themselves questions about their data requirements : Namely, why they want to collect certain types of data (to learn more about customer interactions, for example) and what they hope to achieve.

2. Collecting data: Once businesses are clear in their own mind about the purpose of data analytics , they then need to determine which data sources they'll be using, which data points they'll be concentrating on and how to collect that data. Some simply use transaction and social media data, while others use high-tech sources including GPS and RFID chips.

3. Ensuring data is relevant: We mentioned earlier that raw data tells us very little at first glance. Businesses have to ensure that the quantitative data they collect is relevant and that they know how to make sense of it. Simply hoovering up huge quantities of data does little good-and may prove to be actively counter-productive.

4. Making effective use of data: Businesses which intend to deploy data analytics must think carefully about how they intend to do so, and provide sufficient resources for the purpose. Which metrics do you plan to use? Some businesses employ in-house data analysts , which can give them the edge over competitors, but for smaller firms, employing their own data specialists is unlikely to be viable.

5. Presenting data: Earlier, we mentioned the importance of data visualizations in presenting findings and rendering them more comprehensible. Tools like Tableau can help businesses with visualizing data in the form of charts and graphs. These can then make data presentation aids. For instance, in video tutorials and webinars , as well as for the eye-catching infographics that prove popular on sites like LinkedIn .

6. Acting on new insights: It's one thing gaining all these insights via data analytics , but businesses must have an action plan in order to put them to practical use. How might certain findings help your business improve the service it provides to customers? How might you use it to reach new customers?

Data analytics technologies

There's a wide range of data analytical tools that businesses can use, and more are being introduced all the time. Some of the most widely-used data analytics technologies include the following:

1. Predictive analytics: We briefly defined predictive analytics earlier, as using old data to forecast future outcomes. Based on statistical algorithms and machine learning (about which, more later), it provides businesses with a clearer idea of what to expect in the relatively near future, allowing them to prepare and position themselves accordingly.

2. Data management: Collecting data is one thing, but you need to know how to manage it if you're going to make effective use of it. Analytics processes and tools are therefore indispensable for managing the flow of data and organizing it. They can help your business ensure that you have high-quality data and that it's easy to obtain when your teams need it.

3. Data warehouse: A kind of management system used to facilitate data analytics and other business intelligence processes. Businesses can use data warehouses to centralize data drawn from various sources, thus making it more manageable and easier to draw insights from. It can also come to serve as a useful historical record which data analysts can use to make long-term comparisons.

4. Machine learning: A subset of artificial intelligence (AI), machine learning essentially involves the simulation or approximation of human intelligence in completing tasks. As the name implies, it involves applications which can analyze data and learn from it over time, ensuring that it gradually becomes more and more accurate at predicting outcomes.

Why is data analytics so important?

If you're still wondering what all the fuss is about when it comes to data analytics , perhaps this will convince you: Just look at what some of the biggest businesses in the world are doing, and how seriously they're taking it. Firms such as Amazon are eagerly seizing on the opportunities provided by big data to help them know their customers inside out.

Data analytics is widely used in retail today, and in the healthcare sector. The travel and hospitality industries have also enthusiastically adopted data analytics , as it enables them to develop a much more detailed understanding of what their customers want, and how to resolve potential problems.

The stark reality is this: If your business isn't making good use of data science , then there's a high chance that its rivals already are. This could leave you at a serious competitive disadvantage. But if you take advantage of the opportunities data analytics offers you, your business could be the one to steal a march on them.

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Why data analytics are crucial for your business

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We've heard a lot in recent years about the revolutionary effects of and the internet of things ( IoT ). They have indeed had big effects in a number of areas, from retail to digital marketing and healthcare . With so much data at our disposal, however, the difficulty is in knowing how to make use of it.

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As the old saying goes, "work smarter, not harder". And data analytics is the perfect example of how organisations like yours are doing this.

Data analytics is a major force behind today's business strategies, transforming raw data into actionable insights. It's the key to unlocking trends, decoding customer behaviour, and steering companies towards smarter decisions.

Operational efficiency is all about ensuring organisations deliver top-notch products or services while allocating their resources effectively.

This involves optimising processes, resources, and technology to maximise productivity and reduce waste. It means ensuring that every aspect of operations is aligned to enhance performance, lower operational costs, and improve customer satisfaction.

And efficiency is crucial for businesses aiming for sustainable growth and competitiveness in their industry.

So what are the potential benefits of improved operational efficiency for your business? Here are some examples:

Reduced costs

Higher revenue and profitability

Shorter lead times

Better customer satisfaction

Better staff engagement and retention

Higher productivity levels

Greater sustainability

Excited to learn how data analytics can bring you operational efficiency and all the benefits that confers? Let's jump into our list of 5 ways.

1. Use data to drive decision making

Data analytics is crucial in guiding businesses towards more informed decision-making.

The era of relying on intuition and traditional guesswork is gone. And analytics has made room for data-driven strategies, allowing organisations to forge ahead with more confidence than ever.

Using data analytics for informed decision-making begins with gathering and analysing relevant data from various sources. These sources could be customer feedback, sales reports, and market trends.

Analysing this treasure trove of information often reveals patterns and insights that might otherwise remain hidden. By understanding these patterns, you can make important business decisions that are backed by concrete data.

"I knew if we were going to become more data-driven in our decision making, then I needed to equip myself with all the right tools to support the business in making those decisions. So when I heard about the apprenticeship I thought, 'That's exactly what I need!" Lisa Cooney, TrendBible

Embracing data analytics for decision making empowers businesses to replace uncertainty with clarity.

2. Automate your processes

Automating everyday processes is not a luxury. It's a necessity for improving operational efficiency.

Data analytics helps companies shift from manual, time-consuming tasks to automated, efficient systems. This often frees up valuable resources for more value-add or strategic activities.

A crucial tool used in data analysis that makes automation of processes possible is Python. Python's simplicity,vast ecosystem of libraries, and cross-compatibility make it a powerful tool.

Professionals working across functions like marketing, supply chain and customer support are writing Python scripts to automate tasks like report generation, inventory management and even email marketing. And this automation leads to reduced manual effort and errors.

Elle Neal , Data Scientist from BPA Quality saved her organisation 48 hours per month by switching from manual Excel spreadsheets to Python for data processing work. Elle also simultaneously increased leads by 13X. This is a fantastic example of how using data analytics can boost operational efficiency.

Organisations adopting data analytics for better efficiency have also pointed to impact on larger, more complex data processing tasks:

"The stuff that really caught the attention of the management team was a lot of the automation work the team was doing-using Python scripts to automate very complex, large data processing jobs. Automating manual processes and creating ML-powered recommendation engines was where we were freeing up a lot of time for the teams and very quickly making an impact and ROI." Jonathan Wagstaff, Director of Market Intelligence at DCC Technology.

With all the time data analytics is saving organisations today through automation, it's easy to see why "Data Analysts and Scientists" roles are in the top 10 jobs expected to grow fastest between 2023 and 2027.

3. Optimise your resources

Optimising resources is not just about cutting costs but also maximising value from all of an organisation's assets. And data analytics offers you the tools to elevate your operational efficiency on both fronts.

Data analytics can help you identify both areas of excess and untapped resources-from talent to inventory and beyond. Once you've gathered insights from across your organisation, you can then allocate resources more effectively to ensure that each asset is used where it brings the most value.

For instance, analytics can reveal the most productive times and conditions for manufacturing operations, leading to energy savings and increased output. In human resources, data can identify skill gaps and optimise workforce deployment, enhancing productivity.

Joel Hollingsworth , Electrophysiology Co-ordinator at the University Hospitals Coventry and Warwickshire (UHCW) NHS Trust, used his data analysis skills to uncover a better strategy for coordinating procedures around recovery times and nurse schedules.

Because of Joel's upskilling in data analytics, the Trust now prioritises procedures with longer recovery in the morning, so patients can receive their full recovery care time without nurses having to work overtime.

Ultimately, data analytics transforms resource optimisation from a guessing game into a precise, strategic process.

4. Use predictive analytics for forecasting

Predictive analytics empowers businesses to anticipate the future, rather than merely reacting to the past and present. This forward-thinking approach enables companies to streamline their operations, minimise risks, and seize opportunities ahead of the competition.

"We do quite a lot of reporting using past data to find trends. But learning tools like predictive modelling will help us use data to improve our forecasting and optimise working capital." Guillermo Dominguez, Purchasing Finance Analyst, Exertis

Predictive analytics leverages historical data patterns to forecast future trends, demands, and events. By applying machine learning algorithms and statistical models, businesses can predict customer behaviour, market dynamics, and potential operational bottlenecks.

This foresight allows for proactive decision-making, optimising everything from inventory levels to workforce allocation, ensuring resources are perfectly aligned with future needs.

For instance, David Howell , Joint Director for Strategic Insight and Analytics at Surrey Heartlands Health and Care Partnership, reliably predicted 70-80% of emergency readmissions by building a machine learning model.

By predicting these emergency readmissions, Surrey Heartlands Health and Care Partnership had an even better chance of preventing them, saving time and resources.

This strategic application of data analytics not only enhances operational efficiency but also drives a competitive advantage, positioning organisations for success in a data-driven future.

5. Make your data more accessible

Data has been dubbed "the new oil", fuelling businesses towards unprecedented growth and efficiency. Yet, the true power of this resource lies not just in its collection but in its accessibility across an organisation.

Data analytics helps make data more accessible and a game-changer for operational efficiency, democratising information and empowering decision-makers at all levels.

And apart from expanded access, employees can be confident that they are all working with the same data.

Data accessibility tools and platforms, such as dashboards and centralised databases, enable staff to retrieve real-time insights relevant to their roles without the bottleneck of gatekeepers or IT departments.

This immediacy in accessing data means that responses to market changes, customer feedback, and internal process adjustments can be fast and based on the latest information.

Accessible data fosters a culture of transparency and collaboration. Teams can easily share insights, identify trends, and work together on data-driven strategies. It also encourages innovation, as employees at all levels have the information they need to propose solutions.

Enhancing the accessibility of data within a business does not just streamline operations. It transforms the organisation into a more agile, informed, and cohesive unit.

Conclusion

The transformative power of data analytics in enhancing operational efficiency is undeniable. Organisations can flourish by embracing strategies such as automation, resource optimisation, predictive forecasting, and making data more accessible. And in doing so, many such organisations significantly improve their productivity, reduce costs, and strengthen their competitiveness.

Are you or your team interested in upskilling in data analytics? Improve your organisation's operational efficiency today and get in touch via the form below or learn more by taking a look at our Level 4 Data Analytics apprenticeship.

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5 Ways Data Analytics Can Boost Your Operational Efficiency

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Data-Driven Decision-Making: Why Analytics Are Crucial for Business Success

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In the business world, decision-makers must rely on their own knowledge and intuition to some extent. Of course, that can only get you so far. In today’s era of big data, more businesses are relying on analytical insights to make decisions.

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Why Data Analytics is the Key to Business Growth in the Digital Age - StackRoute Learning

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Current digital trends force businesses to create massive amounts of data continuously throughout each second. Data forms the backbone of contemporary