Platt Perspective on Business and Technology

Building a startup for what you want it to become 32: moving past the initial startup phase 18

Posted in startups by Timothy Platt on May 11, 2018

This is my 32nd installment to a series on building a business that can become an effective and even a leading participant in its industry and its business sector, and for its targeted marketplaces (see Startups and Early Stage Businesses and its Page 2 continuation, postings 186 and loosely following for Parts 1-31.)

I have been focusing in this series, since Part 28 on the increasingly important role that data, and that big data in particular are assuming as drivers of even just basic competitive business strength and for an increasing range of industries and business sectors, and when addressing an increasing range of markets that they would serve. And I have reached a point in that line of discussion (in Part 31) where I have begun considering the role that with-in business developed, and third party sourced business intelligence have come to assume in a cutting edge innovative business context.

I have been focusing on what is ultimately consumer sourced data in this progression of postings: Part 31 included, and on both individually identifiable and more anonymized data in that. And that narrative progression has led me to a to-address list of points that I will work my way through in what immediately follows in this series:

1. An at least brief discussion of businesses that gather in, aggregate and organize information for other businesses, as their marketable product and in accordance with the business models of those client enterprises.
2. The questions of where all of this business intelligence comes from, and how it would be error corrected and kept up to date, as well as free from what should be avoidable risk from holding and using it.
3. And that will mean addressing the sometimes mirage of data anonymization, where the more comprehensive the range and scale of such data collected, and the more effectively it is organized for practical use, the more likely it becomes that it can be linked to individual sources that it ultimately came from, from the patterns that arise within it.

Then after considering these issues and discussing them in at least selective detail for purposes of this series, I will use that narrative as a foundation for further considering in-house versus cloud based systems, and for acquisition, processing and validation, organization, storage and use of all of the raw data and processed knowledge that arises here. And I will proceed from there to consider these issues from a more business-development timeline perspective, bringing in the issues and challenges of cost-effectively developing a business for all of this and how and when, so as to more effectively support change and scalability while controlling possible risk. My goal there is going to be one of tying all of this discussion back to a startups and early-stage business context.

But before turning to that second complex of issues as just noted in the above paragraph, I will address the three above-offered, more immediate topics points, beginning in this posting with an at least starting discussion of Point 1. And I begin that by citing two relevant series of postings as background references, that I initially offered in this blog as a consequence of conversations that I had with colleagues:

• Big Data and the Assembly of Global Insight out of Small Scale, Local and Micro-Local Data (as can be found at Reexamining the Fundamentals as Section IV), and
• Mining and Repurposing of Raw Data into New Types of Knowledge (as can be found at Ubiquitous Computing and Communications – everywhere all the time, as postings 156 and following.)

I begin discussing the third party source businesses of Point 1, that provide business intelligence as business-to-business providers, by roughly dividing them into two basic categories:

• Specialized, or niche market big data providers, and
• Generalist big data providers.

The development and proliferation of big data and of big data opportunities, has created a large and varied business sector, and even an entire industry of business-to-business data aggregators and organizers that buy and sell business intelligence as their own value-added marketable commodity.

I have cited specific target-industry examples of this phenomenon in this blog, that would fit a more niche provider business model, to describe them in terms of the above-offered dichotomy. And one such specialized niche business intelligence provider type, that I have made note of several times in this blog is comprised of automotive retail business-supporting sales leads aggregators that service the needs of automotive retail businesses for car and truck sales: each claiming to be the best in their marketing and sales area for pre-qualifying their leads offered as current and as representing potential local customers who could afford to buy and who are in the market to do so.

My point there, is that these big data aggregator businesses, offer value added data that they would argue, their automotive sales dealerships could not cost-effectively match from their own data collection and filtering and vetting efforts. And they all claim to offer the best such sales lead data that would most easily be convertible into completed sales from their value-added data filtering and processing activities. And yes this data, and for most such businesses would include both anonymized and personally identifiable customer and potential customer data and in large and varied quantities. And it would be organized and offered in ways that would connect with their client business’ business models and their sales and marketing catchment areas.

I am going to discuss this working example, niche information provider type in my detail in my next installment to this series. And I will also discuss the bigger and more widely involved players in this overall industry: generalist big data providers there too. In anticipation of that, I will discuss Google, Amazon and Facebook for how they are positioned to provide this type of service, and Facebook as a particular case in point example for how they have built so much of their basic business model around monetizing user-sourced data as their primary source of incoming revenue. They sell targeted marketing and sales opportunities; this means they sell data related to and coming from their registered users, and access to the results of their big data analysis and processing of this data.

Meanwhile, you can find this and related material at my Startups and Early Stage Businesses directory and at its Page 2 continuation.

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