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Vinay Mehendi
Vinay Mehendi, PhD focuses on customer engagement, alternative data, technographics, and CRM data management. he was listed as Top 20 B2B Marketer by Ruth Stevens in 2020. He is a Clemson graduate.
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Business analytics is a data management solution and business intelligence subset. It uses data mining, predictive analytics, and statistical analysis methodologies to examine and convert data into useful information, recognize and predict trends and outputs, and ultimately make wiser, data-driven business decisions.
Business analytics refers explicitly to the following:
• Taking in and processing historical business data.
• Examining that data to recognize patterns, trends, and root causes.
• Creating data-driven business decisions based on those insights.
You can target almost every decision-maker because they all need business analytics.
Business analytics is quickly sold when you talk using domain knowledge.
Business analytics software – the visualization part takes little time, but building data pipelines and making them better and more efficient is a never-ending job.
Return on investment from analytics projects takes a lot of work to come by. It depends on many factors rather than on the software itself. Also, artificial intelligence (AI) technology is changing so fast that it is challenging to keep up with it.
You can use domain-level words when talking to non-data engineers or non-technology folks. However, you must bring your solution expert if you are talking to technology or product folks.
The software and solutions that are heavy on domain and data science are the ones that are in demand.
A channel partner route to acquire these customers varies by industry.
Big data analytics analyzes large amounts of data to uncover covert patterns, correlations, and additional insights. With the help of today’s technology, it’s possible to examine your data and get answers from it immediately.
Data is the primary foundation in both Big Data and Business Analytics. While Big Data deals only with large volumes of a variety of data, business analytics do not have this rigid constraint. It looks at a much larger perspective of business value, and anything that helps derive value from it is welcome. Source data for business analytics can be traditional, and big data is stored in relational excels and databases. Business analytics is also responsible for finding ways to acquire data.
Traditional business analytics and the Big Data paradigm focus on deriving insights from data. After a series of complicated data pipelines for a Big Data platform, deriving insights can be the last step.
The demand for business analytics professionals continues to grow along with big Data. Over the decades, various abstract information produced by the internet accelerated significantly faster over the past few years. Primitive database management and analysis techniques are no longer sufficient, especially where unstructured data like social media posts and product reviews are concerned.
If a company is already using business analytics, they have a process and tools to build their ETL pipeline, transform and process data and render meaning out of it. The question here is, if a company is already using business analytics, how will you sell your software to them?
Everyone has business analytics tools these days. You will have to dig deeper, as knowledge of business analytics tools will tell you the technology stack they follow. Here are the things you now know:
1. These companies have a large data volume.
2. They have ETL processes that could be working just fine, or they need improvements.
3. These firms seek insights and recommendations and are mature analytical customers and users.
All of this signifies:
1. They have good cloud spend because they must run significant jobs to process their data.
2. They can always look for better algorithms and more thoughtful decision-making in their business analytics.
3. If they are using business analytics, they are looking for efficiency. Thus, they will understand compliance, test automation, and customer success. They would be more acquainted with new-generation technologies, so you are talking to a more knowledgeable customer.
Companies using business analytics are looking for better recommendations. Their pain point includes that:
• Data is there, but no insights are being derived from it.
• Data and insights are available but not specifically beneficial for their business.
Hence, connecting insights and actionable insights is quite challenging. Finding insights related to customers, revenue, or cost savings is also a lot of work.
The type of analytical software that they are trying to use can give us the following indications:
1. If companies are using Tableau, they are connecting it to various databases. They have ETL in place and a platform that many users are using.
2. If they have powerBI, they are more accepting of Microsoft technologies than other technologies.
3. If they have a Python-based dashboard, they have in-house data scientist teams. Their highest priority is not just focused on having a dashboard. They are also inclined towards machine learning.
Hence, connecting insights and actionable insights is quite challenging. Finding insights related to customers, revenue, or cost savings is also a lot of work.
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