B2B Branding & Marketing by Bejoy Peter · 2023-05-30 · 17 min
Key moments - from our scoring
Substance score
38 / 100
Five dimensions, 20 points each
Business intelligence represents a fundamental shift in how manufacturing and engineering companies approach decision-making, moving from intuition-based forecasting to data-driven strategy. Agrim Yog explains that BI leverages technology and analytical tools to collect data from multiple sources - sales records, production metrics, market trends, customer behavior - and transform it into readable, actionable insights through dashboards and visualizations. For small enterprises (SSIs) without ERP or CRM systems, he recommends starting with Excel or Google Sheets and their pivot functions before graduating to cloud-based platforms like Power BI, Tableau, or QlikView. The implementation requires organizational mindset shifts: continuous data input discipline, collaboration between IT and sales/marketing teams, and trained personnel to maintain the infrastructure. Yog acknowledges that Indian SMEs and manufacturing companies lag behind developed markets in BI adoption but are beginning to recognize its value, particularly as they seek competitive advantages through accurate sales forecasting and customer insights. The path forward integrates existing ERP/CRM systems with BI tools, and eventually incorporates AI for predictive analytics and automated insight generation.
Business intelligence uses analytical tools to gather data from various sources and transform it into actionable insights through visualizations and dashboards that support data-driven decisions, whereas ERP and CRM systems primarily serve as data storage repositories; BI is about interpreting and acting on the data you've collected.
Start with Excel or Google Sheets to collect and organize data using pivot tables and basic graphs, then extract and make that data readable; only after establishing data collection discipline should you move toward adopting formal CRM/ERP or cloud-based BI tools.
Cloud-based BI tools like Microsoft Power BI, Tableau, and QlikView offer affordable solutions with nominal fees and enable advanced analytics on customer behavior, purchase history, and sales forecasting without requiring extensive IT infrastructure.
Expect initial disruption for approximately six months as the organization adopts continuous data input practices and develops data-driven decision-making habits; success requires training staff, ensuring system integration with existing ERP/CRM, and maintaining management support for the new processes.
Yes, corporate companies in developed countries extensively use BI tools like Power BI and Tableau, while Indian engineering and manufacturing SMEs are still in early adoption stages, though some larger companies are beginning to implement these systems and analytical tools.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode covers foundational BI concepts (definition, tools like Power BI and Tableau, data collection workflow) but largely reiterates industry-standard thinking without novel or counterintuitive claims. Most insights are predictable: BI requires data, improves forecasting, needs organizational buy-in. The host-guest dynamic is conversational but lacks depth - they agree frequently without pushing into less obvious territory.
BI is business intelligence. So it refers to using of technology or using of analytical tools to gather data from various sources
you need to have those data sets in place so that you can at least extract them and then transform them in the readable data
The discussion follows a well-trodden path: BI basics → tool recommendations (Power BI, Tableau, QlikView) → implementation challenges → AI as the next frontier. No contrarian views, first-principles reasoning, or surprising frameworks emerge. The framing of BI as 'moving from good to great' is familiar management-speak. The mention of AI is surface-level and speculative ('AI is still in the talks. It has not, not been implemented so much in the market').
there are cloud based BI tools, mainly Microsoft, uh, Power BI, Tableau, QlikView
introducing business intelligence or a BI tool in the organization is a step from moving from good to great
Agrim appears to be a BI consultant or advisor but provides no credible signals of deep operational scale. No company names, customer base size, revenue impact, or concrete deployment experience are mentioned. The guest sounds knowledgeable but sounds more like an analyst or consultant who has studied the space rather than a practitioner who has driven BI transformation at a sizable organization. No evidence of hands-on execution at scale.
I have seen some of the small analytical tool companies who are trying to help these SME owners
I am seeing that trend
The episode is almost entirely devoid of concrete examples, numbers, metrics, or named case studies. Tool names (Power BI, Tableau, QlikView, Excel) are mentioned but no outcomes, timelines, cost figures, or customer results are provided. Claims about accuracy improvement and forecasting benefit lack supporting data. The closest to specificity is a generic reference to segments (SSI, SME, corporate companies) without any data backing.
these cloud based BI tools, they have a very nominal fee
you can plot all of these things and probably you know have a very uh big see through in the sales pipeline or the sales forecast
The host asks structured questions and builds logically through BI fundamentals, implementation, and future trends. However, the questioning lacks follow-up depth and challenge. When the guest makes vague claims (e.g., 'AI is still in the talks'), the host accepts them and moves on rather than probing for specifics. The exchange feels cordial but surface-level, with frequent agreement signals ('Right,' 'Correct') rather than productive tension or rigorous follow-up.
So I mean if we take a case of like a SSI where I mean it's a very small enterprise, right
What is step number one that he does in the next one hour?
Computed from the transcript - who did the talking, and the words that came up most.
Send us Fan Mail Business intelligence helps extract crucial facts from a vast amount of unstructured data and transform them into actionable information that enables companies to make informed strategic decisions, improving operational efficiency and business productivity. In this episode, I am in discussions with Agrim Yog a professional with over of a decade of marketing experience coupled with a great acumen in information technology. We hope our discussions serve as a conversation started in the field of Business Intelligence (BI) and the tools that drive the same. Do share your feedback with me on bejoy@VisionKraft.com or speak to me on 9850555795 Thanks for tuning in to our podcast today! We hope you enjoyed the conversation. If you have any questions, comments, or would like to engage further, feel free to reach out to Bejoy Peter. You can write to him at bejoy@visionkraft.com or give him a call at +919850555795. We appreciate your support and look forward to hearing from you. Until next time, take care and keep exploring!
Transcribed and scored by The B2B Podcast Index.
Speaker A: Foreign.
Speaker B: Hello and welcome to the podcast on industrial marketing by Bijoy Peter. With an experience of over 20 years, Bijoy helps engineering and manufacturing companies implement modern marketing programs that deliver uh, meaningful business outcomes through definitive brand strategies, production of media assets, marketing automation and lead generation systems transforming their digital footprint globally. Hope you find the podcast m valuable to your business. Do visit B E j o y B-E-T-E-R bjoypeter.com to stay connected.
Speaker C: Hello everyone. Uh, welcome back to my episode on BI for sales and marketing, um, which is an extension of uh, or maybe an aggregation of a lot of episodes that we've done earlier. We, that is me and Agram. Yes, uh, welcome Agram.
Speaker A: Yeah, thank you.
Speaker C: The idea of this episode is to uh, aggregate uh, the content that we have delivered in our earlier episodes. Right. Um, most of the assets that are created in media production creates data.
Speaker A: Correct.
Speaker C: And um, this episode is about what do you do with the data.
Speaker A: Correct.
Speaker C: So uh, before I start the episode, Agram, I would like to clear some basic fundamentals with bi. So what is uh, bi?
Speaker A: Yeah, right. So you know, BI is if you uh, just break it down, it's business intelligence. So it uh, refers to using of technology or using of analytical tools to gather data from various sources. Now in an engineering or a manufacturing industry, so you have various sources of data, like you have the sales data, uh, you have the production data, you have the market trends. So using all this data, uh, and transform it to make it a readable data.
Speaker C: Like an actionable data.
Speaker A: Yeah, it's an actionable data so that from that data you can interpret and you can uh, make an informed decision. So by informed decision I mean that you are reading the data, you are uh, plotting charts, you are plotting graphs and you have that visualization dashboard and based upon that you make a data driven decision. So in terms of engineering and manufacturing industry, so they have numerous data points, they have data points in production about the availability of raw material. What are the marketing trends then? They have the sales data, they have the customer behavior data, they have the marketing trends. So based upon this they can make a future decision.
Speaker C: So I mean if we take a case of like a SSI where I mean it's a very small enterprise, right. Um, let's say they uh, do not have um, ERP or maybe they do not have a CRM. So uh, in that scenario do you recommend that, ah, the first step towards BI is to adopt ERM and CRM to adopt a ERP or a CRM?
Speaker A: Certainly. So you know The CRM and ERP could act as a data storage but however that is not the point of these uh, tools. But uh, certainly for early adoption stage they could certainly act as a data storage and then later when you extract the data. So at least you have those data point sets available, readily available and later then you can move towards adopting business intelligence. And I would say that yes there are a lot of uh, tools available in the market. So the earliest tool I would suggest is the spreadsheet or the Excel or the Google Sheets. So they have a very wonderful function of pivot. Yes, they do not provide very advanced analytical tools that is not uh, currently applicable in an early adoption stage. But however they can use that pivot uh function and probably plot tables according to the segregations or they can plot very simple graphs. So this would be very beneficial at an early stage of adoption. But previous to that you need to have those data sets in place so that you can at least extract them and then transform them in the readable data.
Speaker C: Yeah, so this is the case of uh, SSI. Now in case of an SME, I've uh seen that most SMEs do have a CRM or ERP. Ah, uh, what do you recommend for them? I mean for them is Excel again start point or what is the tool that they need to purchase or invest into.
Speaker A: So uh, uh spreadsheets, yes they are readily available and that can be used too. But however if they want to take a step ahead and uh, up their game, there are cloud based BI tools, mainly Microsoft, uh, Power BI, Tableau, QlikView. So these cloud based BI tools, they have a very nominal fee and they can be used to uh build advanced type of uh, decision making, uh graphs or charts and you know uh, in terms of customer demographic, in terms of customer behavior, what is their purchase history? So you can plot all of these things and probably you know have a very uh big see through in the sales pipeline or the sales forecast. Okay so these are the tools so you can have a uh, very basic infrastructure, BI infrastructure in place at your company and uh, probably uh, a person should be responsible to handle this infrastructure and maintain it as well.
Speaker C: So uh, uh now as, as we are getting into this interesting conversation, I'm realizing that uh, to implement uh, business intelligence into sales or production or the business at large, uh does the business owner need to hire uh, someone or appoint somebody in the company who can um, engage in creating this data or rather confirming this data?
Speaker A: Yes. So uh, either they can train their salespeople or their marketing people to Know the benefits of BI because uh, it is all about continuous data inputs. So either they can train them or they can hire them to maintain this infrastructure. So there has to be a collaboration between the IT team and the sales team or the marketing team so that there's a continuous data input, there's a continuous feedback uh, from the data input. And uh, they need to understand what is the, how to read the data firstly. So uh, based on the uh, you know, uh, trainings and how to read the data and maintaining that infrastructure they can surely uh, come up with a very basic infrastructure for the bi.
Speaker C: So essentially uh, this uh, introducing uh, business intelligence or a BI tool in the organization is a step from moving from good to great.
Speaker A: Great. Yeah, exactly.
Speaker C: Um, so obviously to move from good to great it needs a certain type of mindset that needs to be adopted. A lot of businesses are doing really good, uh they see uh, a uh, really good future in engineering and manufacturing. Um, so what kind of mindset change is required at the decision maker level? I mean how should they see this uh, as an investment or how should they see this that if I don't have, then this is what I'll miss on from a tangible to an intangible perspective.
Speaker A: So generally uh, you know uh, at the end of or at the beginning of the new financial year these um, management sits together and decides on a forecast. So they predict the market uh, via their own uh, you know, studies or individual studies.
Speaker C: Right.
Speaker A: So based upon that maybe uh, the accuracy levels are not that high.
Speaker C: Right.
Speaker A: So to increase that uh, uh speed of uh, knowing the uh, sales forecast and what would be the marketing trends, what is the customer behavior based upon the purchase history, what is popular, what is not popular. Based upon that they can have a very accurate forecast which they might not have from whatever studies or whatever things they have read on the Internet. So you have data available. So through that they can surely uh, make these uh, decisions better.
Speaker C: We are having this filter coffee from Versus Money. So if we pass this mention, will they pay us something?
Speaker B: I'm sure.
Speaker C: Great. Um, so um, since this is a uh, new adoption, this practice of implementing bi, obviously it will come with this initial learning curve that will include a certain set of challenges. What do you see as the initial set of challenges while this is being implemented? Like we know organizations, when they try and implement SAP for the first six months, uh, it's a major change and things go haywire, stabilize it. So in the case of a bi, how much disruption do you see in the implementation?
Speaker A: So firstly they need to uh, verify that whatever ERP or CRM systems they have, it could be integrated with the BI tool which they are introducing. So suppose they have those spreadsheets or the Power bi. Maybe uh, CRM or ERP do not have that data extraction facility. So they need to first implement that and then come up with the BI tool. Further than that. Then as you said that there should be a change of mindset. There's uh, a continuous input should be given to the uh, BI tool, uh, for analytical tools so that they can read the data and come up with the decision.
Speaker C: Right.
Speaker A: So these are the two major.
Speaker C: So we are looking at disruptions in the initial six months which leads to long term needs. Yes.
Speaker A: So there should be some encouragement, some motivation given to the sales people, to the management that you know, there should be continuous input and they should be data driven decisions. You know, we should make a habit of creating a data driven decision. The sales people can look at the bi, uh, whatever charts they are forming, whatever data points are interesting and based upon that they can create a sales forecast rather than, you know, uh, going in the market. Yes, they should go in the market, talk to the customer and get them some idea. But however they can have a BI or a data driven decision as a helping point for them and um, make a better calculative decision based upon uh, while creating a sales forecast.
Speaker C: Uh, you did mention right now continuous input. Yes, uh, continuous input, a predictable output. This form of um, uh, this form of doing business has been largely, or is being largely replaced by what we call as artificial intelligence.
Speaker A: Correct, correct.
Speaker C: Uh, so artificial intelligence and business intelligence put together. Uh, how do you, how do you narrate picking up AI and then producing bi.
Speaker A: So AI, you know, it's still in the talks. It has not, not been implemented so much in the market. But however, if you want to really go up in the game, so surely AI needs to be implemented. So BI is all about collecting data and then representation. It's all a uh, manual work.
Speaker C: Right.
Speaker A: However, if you want to automate this process of input and uh, doing uh, that analysis, AI needs to come into place.
Speaker C: Okay?
Speaker A: So AI have certain algorithms that you can implement and then based upon that it can do a predictive analysis.
Speaker C: Right.
Speaker A: Based upon the data of previous sales marketing trends. Even you know, it can collect data through the Internet, study the market trends ahead, what, what's going to be the economy situation. So based upon that it can create a very uh, nice dashboard or maybe a very nice forecast for you. So that is one of the uh, very uh, nice Way to implement AI. However you need to have that uh, advanced infrastructure in place so that uh, uh, you have people on board who can understand the algorithms, who can code for you and ultimately uh, integrate it with the bi.
Speaker C: Right. So uh, as I see this, uh, the way forward for a lot of SMEs and SSIs is to eventually adopt uh, information technology.
Speaker A: Yes.
Speaker C: As a part of their business practices and you know, focusing on uh, the extension of their existing ERPs, uh towards AI and then building the business intelligence.
Speaker A: Exactly. So you know they do have the CRM, the ERPs, they can collect the data, but if you don't know what to do to do about that data. So there is no point.
Speaker C: Yeah.
Speaker A: So I suggest that start collecting data on a day to day basis, on a monthly uh, basis, on a quarterly basis, probably study the data on a quarter wise so that it's a very small exercise for you. And then after collecting the data, transform the data, make the data readable. So based upon that then they can start their BI journey.
Speaker C: Uh, are Indian businesses, uh, uh, I'm not aware of whether this uh, business intelligence as a tool being integrated is a normal in more developed countries or are Indian businesses a little far away from uh, bringing this practice into normal? I mean what is the trend abroad and how soon can India catch up with it?
Speaker A: I believe that uh, yes, you know, uh, if we talk about companies uh abroad they surely talk about BIS and business intelligence and they use these tools extensively, specifically power, BI and tableau, which are the top two tools. So uh, if you talk about the corporate companies in India, yes they are probably have started to implement but in terms of engineering and manufacturing industries they are yet to come on board and I am seeing that trend. Yes. You know they have uh, started giving some significant importance to CRMs to ERPs so that you know, they can have those basic charts in place, basic data in place in one point.
Speaker C: Right.
Speaker A: So they are trying to use those cloud based tools as well. But currently if we see in the terms of SME owners, I have not seen me and this trend sort of. But yes, I have seen some of the small analytical tool companies who are trying to help these SME owners. But still it's at a very early stage.
Speaker C: Okay, so let's say our audience who's listening to this episode today, um, uh, what does he do in the next one hour? Let's say he's convinced about the idea. What is step number one that he does in the next one hour?
Speaker A: Uh, so I feel extract the data first. You try to implement CRM and erp. Study a bit about business intelligence and then uh, get the data out, talk to people around, uh, what sort of data is important, what are the marketing trends and based upon that, try to read out that data. Uh, maybe if you don't have that expertise at place, uh, you can uh, search for an agency you can help or if you feel that, okay, there is someone who is enthusiastic about it in your organization, train them. Yeah. So probably you can have that first step in place.
Speaker C: I'm glad you didn't say that. Call for a meeting first. On that note, Agram, uh, thank you for um, I mean sharing your expertise and your point of view on how the future of businesses will look here for uh, and helping us understand the importance of uh, business intelligence and AI coming in together. Thank you so much Agram.
Speaker A: Yeah, thank you so much.
Speaker B: Bye Bye. Hope you find the podcast valuable to your business. Do visit B E J O Y P E T e r b joypeter.com to stay connected.
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