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Shyam Prakash Pal

Apr 18 · 5 min read

Kockpit Going Beyond The Power of AI

Analytics powers your marketing program, but how much value are you really getting from your data? And this is where Artificial intelligence (AI) can help you with a broader insight.

This article leans on the expertise to demystify AI. It will give you ideas on using AI for analytics and offer some tools to explore further.

So, What Exactly Is AI?

AI refers to a collection of different technologies that excel at extracting insights and patterns from large data sets than making predictions based on that information. That includes your analytics data gathered from sources like Google Analytics, Automation platforms, content management systems, CRM, etc.

Ask ten different experts on what AI is, and one will find different answers. AI is the science of making machines intelligent. Today, we can educate machines to be like humans. Your smartphone has tons of AI-powered capabilities. These include facial recognition (AI the sees) that unlocks your phone with your face.

This also includes voice assistants (AI that hears and speaks) and, don't forget, predictive text (AI that writes). Other AI systems can give machines the ability to move, one example being self-driving cars. Big whales Amazon and Netflix use AI to offer product recommendations. even Email services like Gmail use AI to write parts of emails for you automatically.

In fact, AI that exists today can help you get much more value out of the data and make predictions about consumer behavior based on it. Now, how great is that?

Is AI the future?

Yes, definitely! The demand for AI in Analytics is here to stay for a long. Investing in the best analytics would take you ahead as the future belongs to Artificial intelligence and analytics.

Furthermore, Machine learning (ML) is a branch of AI that pinpoints patterns based on large data sets. The machine uses these patterns to make predictions. Then it uses more data to improve those predictions over time.

Over the next 5 to 15 years, AI's impact on the world will ramp up dramatically. AI is used in different fields to create more insights and save us time. There is ever-evolving demand for AI in the industry data analytics companies.

Furthermore, According to a report by Business Fortune Insights, the global big data analytics market was USD 206.95 billion in 2020, and USD 231.43 billion in 2021. The market is also expected to be worth around USD 549.73 billion in 2028 at a CAGR of 13.2%.

How is AI used in Kockpit Analytics?

Kockpit Power BI features a unique State Of The Art feature called Goals, which further leverages AI. It, in turn, creates a streamlined and accessible solution for performance management or goal tracking. It gives the ability to a user to track business metrics based on KPIs they prefer, define goals, and measure progress by aggregating the goals via a scorecard.

Here are a few of the top use cases we have found for artificial intelligence in Analytics today:

1- Find new insights from your analytics – Artificial Intelligence excels at finding insights and patterns in large datasets that humans can't see. It also does this at an incredible scale and speed. Today, AI-powered tools exist to answer your website data questions. AI can also recommend actions based on opportunities in your analytics. Some tools to check out here include:

a - Google analytics intelligence
b - Keyence
c - Adobe Analytics

2 - Use analytics to predict outcomes – AI systems that can use analytics data to help you with predicting outcomes and successful courses of action. AI-powered systems can analyze the data from hundreds of sources and offer predictions about what works and what doesn't. AI deep dives into your customers' data and projections. It analyzes consumer preferences, product developments, and marketing channels to generate output.

3- Unify analytics and customer data – AI is also used to unite data across platforms. It includes utilizing the speed and scale of Artificial Intelligence to gather all your customer data into a single, unified view. AI can also unify data across different sources, even hard to crack like call data.

4 - Data analytics is becoming less labor-intensive – Managing and analyzing data depends upon less time-consuming manual effort than in the past. People still play a vital role in data management and analytics, but processes that take days or weeks longer are picking up speed thanks to AI.

5 - AI can help alleviate common data problems – The value of your data and its quality are inextricably linked. Poor quality means less or no value. It is something that big data has in common with AI. If the data is dirty, users cannot trust the insights. The dirty secret of ML projects is that 80% of the time is spent cleaning and preparing the data. Fortunately, users can clean ML data by using AI.

6 - Quick reports - Most business apps are still using that design language of paper forms and ledgers. It means companies capture and stores large amounts of data, and users are still spending inordinate amounts of time slogging through endless reports to find helpful information. The future is an intelligent software that leverages that data to solve problems and provide context.

Kockpit is a powerful tool that gives you detailed analytics about your business's day-to-day operations. It provides complete business transparency by giving you revenue analysis, order analysis, inventory analysis, production analysis, employee productivity, branch productivity, channel partner report, etc. By using Kockpit you can generate and track every business report and productivity.

Kockpit includes a host of features with their revolutionizing pre-designed Kockpit BI dashboard for key stakeholders of an organization for conceptualizing an effective decision-making process. It is an amalgamation of Technology and Vision.

Final thoughts

We can expect many changes in how we handle our big data, edge computing, and analysis over the next decade. As devices become more intelligent, technologies become smaller, and the needs and expectations of consumers continue to grow, the evolution of technologies is imminent.

Shyam Prakash Pal


Chief Decision Scientist, Google. ❤️ Stats, ML/AI, data, puns, art, theatre, decision science. All views are my own.

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