Manish Arora: I started my career in 2001 and have always operated at the intersection of technology and business. I am fascinated by how problems that appeared insurmountable until some years back can now be addressed very efficiently.
I am the founder and co-owner of Suasive Consulting & Analytics. As we are a start-up, I do whatever needs to be done on any given day. We recently set up this business with an objective to deliver cost-effective analytics solutions to clients worldwide. We are focused on English and Spanish-speaking markets. We are for profit and our vision is to make the world a better place, one analysis at a time. We believe that good analyses lead to better decisions and most optimal allocation of resources—this in turn contributes to making the world a better place.
Manish: I was primarily led by a desire to write a balanced article that recognizes the potential of big data analytics and at the same time acknowledges that big data may not be relevant for all businesses in all circumstances. I also wanted to take the opportunity to highlight that analytics are generally a valuable practice for businesses of all kinds and sizes.
Secondly, I wanted to share my point of view with a wider audience based on what I had understood to be the key pain points for SMEs (subject matter experts).
Finally, I wanted to talk about the multi-faceted aspect of big data and big data analytics—to break the stereotypes of use cases. In that, I wanted to demystify big data and make SMEs a little more comfortable when they hear the word big data the next time.
Cameron: OK, so what exactly do you mean when you say that big data has become the proverbial “elephant in the room”?
Manish: What I meant was that SMEs realize that big data is an important topic but many avoid exploring it in further detail and discerning what it could mean for them. So, they end up acknowledging it and ignoring it at the same time. Often times, this is due to an incomplete understanding of the topic and a false impression that big data requires everything big: big bucks, big teams, big businesses, and so on.
Cameron: What are some of the misunderstandings of big data, not just for small companies, but for everyone?
Manish: I would say the following are the biggest misunderstandings that businesses have:
- Big data requires massive investments to be made in storage and processing power—this is not always true.
- Big data analytics means privacy intrusion: If you see all the sources of big data, then many of them are actually not private data [and examples of non-private data are]: public sector data, such as open-data initiatives of government agencies, machine logs, sensor data, public web, blogs, etc. are all big data and can be mined for valuable objectives.
This is not to deny the concerns around privacy and security. They are real, but they are not unique to big data analytics to the extent that these risks remain regardless of whether data is mined or not as part of a big data initiative. We are leading our lives online these days and in many cases, people do end up creating enormous amounts of data. The likes of Google and Amazon leverage this data in creating unique and valuable customer experiences for their users and this is a value that people expect and demand. We can also expect regulators to adapt and advance regulations designed to keep up with the issues related to data privacy and security.
- Finally, there is something that I think is very important to highlight, although it does not directly relate to your question so much: People sometimes understand or imply that big data is the new data model. This is not completely true and takes the focus away from traditional sources of data (think of your good old ERP systems).
This is particularly risky if businesses start to think like this. At its very core, running all business is about being able to answer some key questions. You cannot assess business metrics or evaluate your performance using big data analytics. You need to invest in and maintain your regular sources of data. Microsoft Excel isn’t dead and BI (business intelligence) is certainly not dead.
Cameron: What perceptions of big data do you see when working with smaller companies?
Manish: I think the following are some of the most common ones:
- You have to be big for big data to matter to you—this is probably because the name big data is a little intimidating for small sized businesses.
- You have to be relatively tech-savvy for you to be able to leverage big data—part of this is due to much of the early literature on big data coming from tech vendors.
- Related to the second point, small businesses often think that it is the IT folks who should think of big data related stuff. Instead, I think it should be their marketing, finance or business heads who should lead big data initiatives.
In your article, you point out that a good way for companies to leverage big data properly is to work with companies in their size bracket. Why is that?
Manish: Prior to starting this business, I advised clients on their sourcing strategies. I have learned from experience that your ability to get along with your vendor(s) matters a lot to the overall success of an initiative—this is particularly true for services business, less so for hardware/infrastructure business.
Oftentimes, buyers of services find it difficult to work effectively with vendors who are significantly larger than them. This is because buyers are more likely to attribute any issues in the relationship to the fact that the vendor does not care so much for their business. This may only be a perception in some cases, but perception matters.
Culturally, large vendors develop a way of selling which is not designed with the needs of small businesses in mind. Vendors are likely to feel disappointed that the buyers don’t know how to maximize the value of relationship, while buyers are unlikely to even see those value creation opportunities that vendors think they bring to the table. Many large businesses that were traditionally focused on large enterprises have set up their SME divisions but they have still not been completely successful in shedding the [big] brand associations that their businesses have built over the years.
Finally, going to a “big” vendor for a “big data” project is likely to scare people involved on the periphery. It may just be a psychological thing in some cases but again it matters.
Cameron: You also caution companies to be wary of biting off more than they can chew when it comes to big data. Is that something that happens with a lot of companies? And what things in particular should they look to avoid?
Manish: If you see discussions around Big Data on the internet—there are quite a few sceptics who question the very value of big data by giving examples of companies who haven’t gained as much from the big data investments they have made.
I think what some of these cases really tell us is that the companies probably did not have an overall analytics strategy and even less likely a big data strategy in terms of what they wanted to achieve.
I wouldn’t say that there are a lot of companies who have bitten more than what they could chew when it comes to big data, but there are quite a few who haven’t paid enough attention to the cultural or human aspect of adoption and so their investments haven’t paid off to the extent that they would have liked.
I will give you an example from India. There is currently a construction boom in India as the country is going through a wave of urbanization. Many high end apartments are designed to be what you would call as “smart homes” but some of the features are redundant because of cultural issues.
Unlike in the West, many Indian homes are rarely ever without someone in the home—there are often maids, servants or non working family members in the same home. So you don’t need the input from a temperature sensor outside to determine when to close the curtains and you don’t need your water heater to determine the time that it will take you to reach home in order for it to be switched on automatically.
In most cases, people continue to rely on the human effort to carry out these activities. Also, some of the enablers/complements are just not there—for example, ubiquitous network connectivity, stable power connection, etc. are missing.
So, to answer your question in one line—companies should avoid getting into big data investments that cannot be leveraged due to cultural or human aspects.
Cameron: Your article has a great conclusion about a good team that understands your business being all you need sometimes. In your opinion, with an understanding that both are important, can the right people make more of an impact than the right technology?
Manish: My first degree is in computer science and I started my career as a software engineer—I must confess that I have been guilty of getting too excited about the technology in my early years and neglecting that it is the people who make things happen. So my assertion in a way is the wisdom that I have gathered over the years.
When it comes to being able to formulating a solid big data strategy and executing it faithfully, a good team that can think with its feet on the ground is much more valuable than a team of technical experts who aren’t so close to the business.
Can a good team substitute for the missing technology or compensate for an inadequate technical architecture? Probably not. But a good team will have the ability to recognize where it needs to partner, with whom, and how to maximize the value of the partnership.
I think both technology and people are extremely important and complement each other so it will be inaccurate to give credit to one over another. But it is important to remember that it is people who choose technology, deploy it, and make use of it and in that sense people do take precedence over technology. If you have great people, they will take great decisions related to not just technology, but every other aspect of your business as well.
Cameron: Lastly, you and your company apply a blend the best practices garnered from working with large international businesses. As technology becomes more and more prominent in many job functions, how has this affected the best practices your company aims to apply?
Manish: Well, our USP is that we reflect on our experience of working with large international businesses and use it to develop capabilities where traditionally offshore vendors are weak. We are business led as opposed to being technology led. We sell to CEOs, CMOs and CFOs and not only to CIOs—we believe that you can never be truly transformational if you are selling only to CIOs.
Technology is increasingly more important undoubtedly but we also think that technology companies have done a great job in abstracting things to a level where a non technical person can relate to them. This wasn’t the case so much when I started my career. Remember not too long ago, your business teams could not decide to buy software on their own because they had to consult IT on hardware requirements, network team for the connectivity, and so on. Today, business can decide to buy a SaaS based solution on its own and get it up and running within a few minutes.
The cost of trying new technology has lessened and businesses can adopt a more fluid strategy. We like to keep up with the times and tell our clients that "don’t commit yourself to a technology strategy that is too difficult to change"—I talked about this in my article as well.
Cameron: Thanks Manish. I appreciate you taking the time to speak with us on this.
Manish has always operated at the intersection of technology and business and set up Suasive Consulting & Analytics, a Bangalore based analytics service provider, to blend the best practices learnt from working with large international businesses and observing the Indian service provider industry. Prior to starting his own business, Manish worked as a Principal Consultant with a niche consulting company (ISG), where he advised his clients on their sourcing strategies. Prior to ISG, Manish worked at British Telecom (BT), London as Business Analysis & Planning Director for BT Innovate & Design, where his mandate was to leverage the in-house Business Intelligence capabilities to reduce the operating costs. Manish started his career in 2001 with Amdocs, where he assisted leading global companies on their strategic transformation programmes. Manish holds an MBA from INSEAD (France) and Bachelor of Engineering (Honours) in Computer Science from Punjab Engineering College, Chandigarh (India). He lived and worked in EU and UK from 2001 till 2011 and has been living and working primarily in India and SE Asia since 2011.
About Suasive Consulting & Analytics:
Suasive Consulting & Analytics is a Bangalore (India) based analytics service provider that helps clients worldwide address their business problems through data-driven decision making and leveraging various data assets spread across the organization. We serve a host of industries and are able to deliver all our services in English and Spanish in an extremely cost-effective manner. To learn more about Suasive Consulting & Analytics, please visit http://www.suasiveanalytics.com
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