DATA ANALYTICS

Data analytics is a data-driven approach to decision-making. It is the application of statistical analysis and quantitative methods to discover meaningful patterns and trends in data. Data are collected from different sources, such as business transactions, customer feedback, surveys, sensors, weblogs, and social media posts. Analytics has a wide range of applications, such as predicting customers’ next best action, guiding product development, or identifying fraud. Data analytics has many related processes including data mining, predictive analytics, and business intelligence.

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DATA ANALYTICS SERVICES

Data Visualization

Data visualization is a method of representing data that uses visual components to depict information. Data visualization often makes use of graphs, charts, and diagrams. Data can be displayed with a wide variety of approaches, from basic tables and lists to 3D interactive visualizations. The right data visualization can take your business to the next level.

Data Warehousing

Data warehousing is a technology used to store and extract data from one or more data sources, enabling businesses to analyze the data in an efficient and convenient manner. Data warehouses are designed to provide businesses with access to historical data in order to make better business decisions. This technology allows various departments within a business to share information and work together on projects.

Data Advisory

The term “advisory” is meant to denote an elevated level of expertise. In many cases, the firm providing the advisory service will have access to more information than their client, and therefore may be in a position to steer them toward more productive decisions. Data advisory services can include anything from a government agency receiving consulting on what data would be most useful to collect, to an advertising agency picking up data analysis on the effectiveness of their ads.

Data Migration

Data migration is often used to describe the transfer of data from one system to another, or even from one storage medium to another. For example, migration could be used to move from a legacy mainframe system to a new system running on open-source software. Migration may also refer to the copying of data from a source to a target without modifying or deleting any data in the source.

Master Data Management

It is an ongoing process of matching data in each record with other records which refer to the same thing, in order to ensure that all relevant data is linked together. This enables a consistent view of the item being described by the various records so that they can be analyzed as one entity. The aim is to avoid duplication and inconsistencies in business processes and systems.

Master data management is the process of managing and consistently applying common attributes to related business objects and their attributes, in order to support information interchange and analysis, and enhance data quality.

Data Governance

Data governance refers to policies that help organizations ensure that data are utilized effectively and efficiently, and that information is available when it is needed. It also refers to the controls that are in place to ensure that access rights to data are managed and monitored. There’s a reason why data governance and information management are so important for businesses today. Data is now at the heart of every organization and its success. However, a lack of proper data governance can lead to all sorts of problems including data silos, redundant data collections, information security issues, and more.

Data Quality Management

Data quality management is a process of ensuring that incoming data is clean and accurate. Data quality refers to the effectiveness of the processes that collect and update data, as well as the accuracy, timeliness, completeness, and consistency of the data. Clean data that is effective in accomplishing its business objective can increase an organization’s business performance. Data quality management is therefore crucial in maintaining an enterprise’s competitive advantage.

Data Monetization

Data monetization is a process of extracting value from data, through analysis and optimization. It is essentially the practice of collecting, accessing, and analyzing large volumes of data to derive insights and make decisions. Data monetization can take on many forms in our increasingly digital world, but it essentially boils down to two main approaches: data-driven decision-making and data as an asset. Data monetization is a subset of the broader process of data analytics, which is the extraction of meaning and knowledge from data.

We are providing end to end Data Solutions starting from Data Mining to Data Analytics

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Experience that defines BizProspex

BizProspex is a data solutions company that has been in the industry for 13 years and is still going strong. Over these years, BizProspex has worked with a wide range of clients from various industries including healthcare, finance, retail, and technology. The company has helped these clients to achieve their data-related goals and improve their business outcomes.

We use the latest tools and technologies to ensure that the data we provide is of the highest quality and meets the needs of our clients. Today we stand out as global leaders when it comes to CRM cleaning, Data appending, Data mining, tech install, Data Merging, and Data De-duping for B2B clients.

What do our experts have to say?

Our experts at BizProspex provide accurate and reliable data solutions to their clients using their knowledge and experience. We offer services such as data mining, data cleansing, data appending, data verification, and data enrichment. By leveraging advanced tools and technologies, our data researchers extract insights from large datasets, correct inaccurate data, add missing data fields, validate data accuracy, and enhance existing data with additional information. Our goal is to provide high-quality data solutions that are accurate and reliable for their clients.

FAQ’s

What is data analytics?

Data analytics is a data-driven approach to decision-making. It involves using statistical analysis and quantitative methods to uncover patterns and trends in data.

What methods are used in data analytics?

Data analytics employs various statistical analysis and quantitative methods, such as regression analysis, time series analysis, clustering analysis, and machine learning algorithms.

What are the benefits of data analytics?

Data analytics enables organizations to make better decisions based on insights derived from data, improve operational efficiency, reduce costs, and identify new business opportunities.

What is the goal of data analytics?

The goal of data analytics is to make data-driven decisions based on the insights derived from analyzing data.

Which industries use data analytics?

Data analytics is used in various industries, including finance, healthcare, retail, marketing, and manufacturing, to name a few.

Is your accuracy guaranteed?

Our records come with a 98% accuracy guarantee. In the unlikely event that you come across a lower accuracy rate, you can reach out to our customer relations team, and we will replace the inaccurate data with new data at no extra cost to you. We refer to this policy as our Bounce-Back Guarantee.

WHAT OUR CLIENTS SAY

“We have enjoyed our relationship with BizProspex. They have done a great job in helping us target prospects and delivered 2x times many leads as we expected.”

Steve King

Founder - Consulting firm in Boston

“BizProspex is the only company I would go for the Data Cleaning. The team has a quick turnaround time and produces quality work. Good people to work with.”

Jacob Waldman

CEO - Mcadam Fa.

“Great data; CRM appending partner. I used them twice to append BizSlate data of fashion retailers in NYC. Love working with the BizProspex team, a pleasure indeed.”

Eric Lituchy

Founder - BizSlate