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6 Ways Businesses Can Be Data Driven in 2023

Data is the new oil. The world is going digital fast, and the data race has begun. With the digital transformation of businesses accelerating, organisations are increasing their investments in AI and Big Data technologies to gain a competitive edge.

Some believe that by 2023, 90% of all network traffic will be driven by artificial intelligence as systems continue to learn at a rapid pace. What does this mean for businesses? It’s time to start being data-driven from now on!

Data-Driven Companies

Digital transformation has redefined the role of businesses. They are more data-driven and technology-enabled companies, with their performance measured in real-time and their strategy aligning with their KPIs. The prevailing business environment is fast-paced and dynamic, with technological changes accelerating at a lightning speed. Businesses need to remain agile, innovative and collaborative to stay ahead of the curve.

It is not just about adopting digital technologies but leveraging data to drive business outcomes. Data is the fuel that drives artificial intelligence, machine learning and predictive analytics.

Here are six ways businesses can be data-driven in 2023:

Always be on the lookout for data sources

The first step towards data-driven business is to look for data sources. Enterprises need to go beyond their core business and look for data sources from the broader ecosystem. This is achieved by partnering with startups, data service providers, universities, and government organizations that are focused on data-driven initiatives.

Data partnerships will strengthen your data set and support your ability to quickly pilot and test new solutions. Already having a large data set will allow businesses to build services that can be applied across industries.

Leverage AI and ML to make sense of the data

Once you have a good data set, you need to make sense of it. This is where AI and ML come into play. AI can be applied to every stage of the data pipeline.

AI can assist in data sourcing, preparation, training, and model deployment. AI can help make sense of the data at each stage of the data pipeline by detecting patterns and anomalies, automating repetitive tasks, and providing insights that can drive business outcomes.

Build a data-driven culture

A data-driven culture is essential for businesses to thrive in a data-driven world. This can be achieved by creating the right environment that fosters innovation and creativity. Create a culture where data is celebrated, debated, and democratized.

Leaders should be open to receiving numbers that challenge their assumptions. Data governance and transparency are must-haves for every organization. The data-driven culture will help businesses in leveraging the full potential of data. It will also help businesses in mitigating risks associated with data.

Establish real-time analytics capabilities

Real-time analytics is the need of the hour for businesses to be data-driven. Enterprises are empowered with the ability to generate insights in real-time.

Decision-making can be made on the go, based on the latest data and insights. Data integration and governance challenges can be overcome with the help of technologies like Apache Kafka, Apache Spark, and Apache Hadoop.

Beyond data integration, businesses must look for ways to make data more accessible to the end user. An integrated data approach across data-driven ecosystems and tools will help enterprises in delivering real-time insights and enrich the decision-making process.

Visualisation tools can be leveraged to bring data to life and make it easier for non-technical users to engage with it.

Utilize transparently and trusted data sources

Transparency in data sources will be critical in a data-driven ecosystem. Data quality is the biggest challenge businesses face when it comes to data-driven initiatives. Data quality can be improved by investing in data governance practices.

It is important to invest in data quality and governance to ensure that all data is clean, trusted, and accurate. Data governance can help in achieving high standards of data quality. It can also help in building data trust by making data transparent and accessible to everyone in the organization.

Data Scientist recruitment

The role of data scientists has evolved from being data-driven to data-driven. Data scientists must be more business-driven and have an impact on business outcomes. Businesses must hire data scientists who can provide value to their organization by leveraging data to provide insights and recommendations that can drive business growth.

Data scientists must be equipped with the tools and skills to integrate their findings with existing systems and automate insights for broader distribution. It is also important for businesses to invest in hiring the right talent to make the most of their data investments.

This can be achieved by hiring data scientists who are well-versed with the latest technologies and techniques in the data science field. They should have the skills and expertise to build models that can help in making better business decisions.

Conclusion

Digital transformation has redefined the role of businesses by making them more real-time, data-driven and technology-enabled. To stay ahead of the curve in this new digital world, businesses need to leverage data to drive business outcomes.

This can be achieved by always being on the lookout for data sources, leveraging AI and ML to make sense of data, building a data-driven culture, establishing real-time analytics capabilities, and using transparent and trusted data sources.

5 Min. Read/November 30, 2022/by Team Descasio

5 Big Data Tools Businesses Should Adopt in 2023

Introduction

The use of big data has become more important than ever before. Businesses are using it to make better decisions and get a competitive edge in their industry. In this article, we’ll discuss some of the best ways to use big data in your business so that you can be sure that your organization is using it as effectively as possible.

Real-Time Analytics Tools

Real-time analytics tools are used to process and analyze data in real time. They help companies make better business decisions, improve customer experience, and increase operational efficiency.

These tools can be used for more than just real-time processing – they can also be used on historical data as well!

Large-Scale Data Processing

Large-scale data processing is a powerful tool that can be used in a variety of ways. It’s not just for big businesses, though—it’s also useful for small ones too!

Large-scale data processing involves pulling information from multiple sources and combining it into one large collection that can be analyzed more easily than individual datasets. For example, if you have information about your customers’ purchases at various times in their lives (e.g., what did they buy last month? What did they buy today?), then large-scale data processing will allow you to analyze this information across periods and report on trends over time as well as between different types of products purchased by different customers (e.g., men vs women).

Data Visualization Tools

Data visualization tools are essential for data scientists, who use these tools to visualize their data and make decisions based on the results.

Data visualization tools can help you better understand your data, which in turn allows you to make better decisions. For example, if a business is looking at its sales figures over time and sees that they’re trending downward year-over-year but not by much (i.e., not enough to justify major cutbacks), then creating a plot of those figures might help them see what’s going wrong with their marketing strategy or product design—and maybe even identify ways that could be improved for things improve again later down the line!

Machine Learning Platforms

Machine learning platforms are a type of data tool that can be used to train and deploy machine learning models. They’re also used for creating new models or improving existing ones, which means they have many uses beyond just training neural networks!

As you might expect from the name, machine learning platforms are best suited for large-scale use cases. If you’re looking for an easy way to test how well your algorithm works on a large scale, then you should consider using one of these tools instead of building it yourself. This is especially true if you have experience with neural networks but aren’t sure what kind would work best for whatever application (e.g., image recognition).

Automation & Orchestration

Automation and orchestration tools are used to automate processes, whether it’s data processing tasks or data management tasks. These types of tools can help you process your incoming information more efficiently and make sure that you don’t miss any important updates. In addition, they can also be instrumental in helping you manage your existing data as well as identify new growth opportunities.

To keep up with the latest trends, businesses and data scientists need to adopt the right tools and techniques.

So what does this all mean for business?

It’s simple: the more you use your data, the more value you’ll get from it. And the best way to do that is by adopting open-source tools and techniques.

Open-source software is often less expensive than commercial products but also provides more flexibility in terms of scaling up or down depending on your needs. Real-time analytics tools allow businesses to gain insights faster than ever before—but they still require some level of expertise when using them properly (more on that later).

Large-scale processing platforms help companies process large amounts of data at once while reducing costs significantly over time through efficiencies gained through automation processes like batching or resampling techniques used by some machine learning platforms

Conclusion

We hope you’ve enjoyed our list of the top technology trends to keep an eye on in 2023. We’re covering a wide range of topics, from open-source tools and real-time analytics to large-scale data processing and machine-learning platforms. These are just some of the many ways that big data can improve businesses, but you should never forget that it is important for organizations to stay current with trends to capitalize on this technology as quickly as possible.

4 Min. Read/November 10, 2022/by Team Descasio

Analytics and Reporting, Business Model

What you must do as a business owner to win with big data in 2023

The big data analytics market is predicted to reach $103 billion by 2023, and the market has grown by 20 per cent this year alone. Many companies use big data creatively to gain insights that enable them to become and remain competitive in their field. For example, tech giants like Google, Microsoft, Apple and Meta have successfully leveraged the use of predictive analytics to continually stand out, sometimes designating them as big data companies.

Before we explore how businesses can leverage the power of big data, we must have a firm understanding of the term.

What is Big Data

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IBM maintains that businesses around the world generate nearly 2.5 quintillion bytes of data daily! Almost 90% of the global data has been produced in the last 2 years alone.

Big Data refers to complex and large data sets that have to be processed and analyzed to uncover valuable information that can benefit businesses and organizations.

However, there are certain basic tenets of Big Data that will make it even simpler to answer what is Big Data:

It refers to a massive amount of data that keeps on growing exponentially with time.
It is so voluminous that it cannot be processed or analyzed using conventional data processing techniques.
It includes data mining, data storage, data analysis, data sharing, and data visualization.
The term is an all-comprehensive one including data, data frameworks, along with the tools and techniques used to process and analyze the data.

Types of Big Data

Now that we are on track with what is big data, let’s have a look at the types of big data:

Structured

Structured is one of the types of big data and By structured data, we mean data that can be processed, stored, and retrieved in a fixed format. It refers to highly organized information that can be readily and seamlessly stored and accessed from a database by simple search engine algorithms. For instance, the employee table in a company database will be structured as the employee details, their job positions, their salaries, etc., will be present in an organized manner.

Unstructured

Unstructured data refers to data that lacks any specific form or structure whatsoever. This makes it very difficult and time-consuming to process and analyze unstructured data. Email is an example of unstructured data. Structured and unstructured are two important types of big data.

Semi-structured

Semi-structured is the third type of big data. Semi-structured data pertains to the data containing both the formats mentioned above, that is, structured and unstructured data. To be precise, it refers to the data that although has not been classified under a particular repository (database), yet contains vital information or tags that segregate individual elements within the data. Thus we come to the end of the types of data. Let’s discuss the characteristics of data.

Why Do Businesses Need Big Data Analytics?

Surely, you would be thinking “why should I use it?”.

Undoubtedly, many businesses are unaware of the need for big data analytics. They must know that uncovering valuable insightful information can allow a business to improve value for consumers. It can be done by analyzing patterns in market shifts, trends, customer behaviour, and correlation between data. Presently, 96% of worldwide companies have confirmed the increased investment in Big Data.

Besides, Data devices allow you to carve a whole information scene over the organization. Data investigation is the major perk you can relish. Besides, you can also guard delicate data through Big Data statistics.

The fact is that Big Data is very useful for most businesses, as it allows you to guarantee information well-being and insurance. By enabling advanced software and analytics systems, and having high-power computing systems, big data provides a myriad of advantages to enterprises such as:

Opportunities for revenue generation
Improved customer support
Enhanced marketing strategies
Improved operational efficiency
Competitive advantages, and more

However, amidst all of these advantages, the single question remains looming in the head of business owners that is “How to use Big Data in business?’’

Improve Customer Engagement And Retention

Every business owner understands the value of customer engagement. Surely, your first preference would also be creating an unmatched quality user experience to ensure customer engagement

However, sometimes, it becomes very daunting for businesses to retain customers even with a higher customer base. This happens when you don’t understand the need and desires of consumers.

If you are slow in understanding the desire of your customer, you may end up losing your customers. Here comes big data into the picture. This technology stream allows businesses to analyze and observe customer behaviour, patterns, and trends in real-time.

Simply, the more data a business collects, the more it can find patterns and trends to spruce up user experience.

Currently, it is easy for businesses to collect data which means it will be easy to understand modern-day consumers through real-time data analytic strategies.

Real-time customer analytics is very important to achieve business goals in current market scenarios and Big Data offers that insight.

Gain Marketing Insights

The naked truth of advertising and marketing is that “businesses have lost millions in devising the right strategy to boost the conversion rate. “

Surely, you would have also spent a lot of money in running advertising and still you won’t be able to get fruitful results.

Big data analytics has the utmost potential to improve this operation for your organization by enabling you to match customer expectations. Besides, it also supports changing your company’s product line and devising powerful marketing campaigns.

If you think it is not viable then let me tell you that “ after years of research and cautious enthusiasm, the advertising and marketing technology sector is now embracing big data”.

This sector is now making a more informed and decisive analysis of marketing and advertisements. The major aspects of these analyses are online activity, monitoring point of sales transactions, and detection of changes in customer trends.

Gaining insights about customer behaviour and their buying patterns allows you to understand the likings and preferences of consumers. This similar approach is being illustrated by marketers and advertisers resulting in the creation of targeted and focused campaigns.

You will be able to save money and ensure efficiency by leveraging targeted, and personalized campaigns. As per a report, big data analytics allows you to

Predict Audience
Improve targeted advertising
Develop new content and monetization
Better media scheduling and more

Examples of companies that have used big data for market insight to scale include YouTube and Netflix. If you use these applications, you must be aware of how these platforms send suggestions and also show advertisements. Past searches and the watch history of users allow them to send the right interest to users which is all done through Big data analytics.

Risk Management

Businesses have to deal with several issues over time. Thus, it is essential to be prepared for any unprecedented risk and problem.

To ensure damage control, industries opt for risk management processes. You already know how critical risk management investment is for an organization.

Foreseeing potential risks and finding a mitigation policy is daunting for every enterprise. You may also visit business consultants to find a full-proof risk management strategy for your company.

In a recent couple of years, big data analytics has greatly contributed to this stream for developing robust risk management solutions. Now it can be used in several ways for risk management.

Fraud Management: Big Data systems compare external and internal data and offer faster fraud recognition with damage control caused due to fraudulent activities.
Mitigate Money Laundering: Big Data analysis can provide identification of suspicious transactions faster and alert the organization beforehand.
Credit Risk Management: Big Data includes predictive capacity and provides sources of data to foresee user behaviour for credits and loans.
Operational Risk: Companies have to face several operational risks at times. Big Data provides improved control and knowledge over company interactions with clients to strengthen safety.

Apart from these, Big data can be used for integrated risk management. So, it makes sense to use big data to ensure high-grade security in various company processes.

Conclusion

While the big data concept is redefining the business world, it is essential to know how to streamline strategies and avoid using big data in an unstructured way. Big data can help boost business growth and enables small business to compete with larger corporations.

8 Min. Read/October 12, 2022/by Team Descasio