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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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The big data wave started in 2011, but it was just hype and wasn’t too successful until later. Companies realized that they have a significant amount of data that should be used to generate insights. This could help them differentiate between right and wrong. Hence, the need to store that data arose. That’s where NoSQL and graph databases came into the picture. These databases could store huge amounts of data and process it in a reasonable time.
Most data models need a large volume of data and customers. Traditionally, companies in the BFSI or B2C domain have large volumes of data, and they generate analytics from it to understand customers’ needs and behavior. These companies have customer volume, so they can afford to do it. Data growth is significant for conventional B2C and BFSI customers. Therefore, every company wants to target them. Recently, well-funded SaaS companies are also interested in knowing the best use of data for them.
There are three kinds of companies that have data growth:
1. Traditional companies (BFSI or B2C domain).
2. SaaS companies having a significant volume of customer data. You can identify them by the technologies they are investing in and using, like graph databases, NoSQL databases, machine learning, data architecture, data governance, and deep learning.
3. Companies that invest or start Center of Excellence in low-cost countries like India, Malaysia, Indonesia, and more.
The big data wave started in 2011, but it was just hype and wasn’t too successful until later. Companies realized that they have a significant amount of data that should be used to generate insights. This could help them differentiate between right and wrong. Hence, the need to store that data arose. That’s where NoSQL and graph databases came into the picture. These databases could store huge amounts of data and process it in a reasonable time.
Most data models need a large volume of data and customers. Traditionally, companies in the BFSI or B2C domain have large volumes of data, and they generate analytics from it to understand customers’ needs and behavior. These companies have customer volume, so they can afford to do it. Data growth is significant for conventional B2C and BFSI customers. Therefore, every company wants to target them. Recently, well-funded SaaS companies are also interested in knowing the best use of data for them.
There are three kinds of companies that have data growth:
1. Traditional companies (BFSI or B2C domain).
2. SaaS companies having a significant volume of customer data. You can identify them by the technologies they are investing in and using, like graph databases, NoSQL databases, machine learning, data architecture, data governance, and deep learning.
3. Companies that invest or start Center of Excellence in low-cost countries like India, Malaysia, Indonesia, and more.
To identify companies where you can sell analytics, analytics tools, talent, or staff-related analytics, you should see what software they use.
If they are using analytical software, business intelligence software like Tableau, or Microsoft Power BI, or they have large data volumes or both; they might need talent in analytics.
If they are using software like Pentaho, they obviously have large data volumes, and good spending capacity as Pentaho is very expensive. It, however, doesn't mean that Power BI is not a big user. Companies like Mercs use Power BI and are very heavily reliant on it. Companies that want to experiment will go towards Tableau. Now Salesforce has acquired Tableau, so it is also becoming common. These are all traditional BI software.
If you see companies spending on ETLs, it means they have large data volumes.
If you see them spending on IBM db2, oracle databases, or Cassandra, it means they are moving to the next generation databases. If they are using IBM db2, it means they could be using many technologies from IBM, and it will be difficult for them to move to other technologies. However, companies such as MongoDB convince businesses to use IBM DB2 and MongoDB too.
Several new generation companies use many open source software like Apache Solr to do their processing. All of this indicates that these companies with large volumes of data are ok with using open source software. They are even willing to push boundaries and not depend on just Oracle, imdb2, or SQL but also try Cassandra and PostgreSQL.
OceanFrogs tracks investment in analytics software by companies. Some of the important technology categories are:
A lot of companies are not depending on just one tool to get work done. In fact, large, and well-funded companies experiment with two or more tools. OceanFrogs know what different tools these companies are using.
The team types, data governance, or customer data privacy of a company tell you a lot about the kind of data they have. You can check their policies if they are very particular about GDPR. OceanFrogs can provide you with all this information about them.
Along with their technology software, you must also see their technology intent. Therefore, you can sell software related to data governance to those that are investing in data governance. All this information is available with OceanFrogs.
You can check our personas-based listings here (link)
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