If you only have a hammer, everything looks like a nail

When Abraham Maslow posited the Law of the Instrument or Golden Hammer theory, in the 60’s, he declared that, “If the only tool you have is a hammer, then everything looks like a nail.” Unfortunately, this is a common response to a recurring problem that is usually ineffective and risks being highly counterproductive.

At a few of the talks at the Gartner BI Summit, I attended last week in Munich, Germany, it became very obvious that different skills in the modern workforce, from data scientists to managers analyzing high-level dashboards, in addition to, different levels of data knowledge with variations of detailed understanding of information, lead to an urgent need of different tools for different use cases. Additionally, if you acknowledge the higher uncertainty of the types of questions that businesses need to ask of data, companies need much more flexibility these days, since specific use cases won’t always be that clearly 
spelled out.

Thanks to the massive sensorization and monumental uptick in data aggregation that the market is experiencing, from the Internet of Things sensors, social media interactions, extensive data inputs, and mobile connectivity, it is fair to say that more spatially indexed data is available than ever before.

However, more and more, we are seeing organizations invest in tools and platforms that are meant to be heavily adopted and used ubiquitously throughout. The growing complexity of the omnichannel business world, together with the proliferation of data sources and real-time decision needs, present a challenge to finding a tool that solves all those requirements and can be used indiscriminately.

These diverse data sources and business roles make it paramount for decision makers to lay down the array of tools that are being used and see how the data and analytical workflow moves smoothly from data input to analysis, up to the final outputs and consumption on the business side. It is simply not enough to start a discovery process with just the data at hand. You have to start with the challenge posed by the decision maker (“I need to know if my customers are unhappy”), then you will be able to provide context around the decision, and make predictions that lead to an action that can be reported.

The humongous amounts of location data combined with the right tools and approach, can help organizations enhance their operational efficiency, target their customers in better ways, and make smarter strategic decisions.

Yet, it all starts with the data scientist, or as CARTO calls them, the “builders”. Traditionally, data scientist have held the hammer. With a data-savvy user base that expands broader and broader, the workflow has to move seamlessly from complex features that require specialized skillsets, to more commonly used dashboards and custom geospatial apps, that can be used as interactive fixed reports, enabling last minute analysis and visual data discovery by the business expert.

Gartner analysts, who are always great at conceptualizing trends and finding the right words to explain them, tell us how the 90’s was the reporting era (with key examples like Cognos or Sap crystal reports), a time where static outputs with a fixed formatting were the norm, with known requirements that rarely changed.

The 00’s saw the coming of the self-service BI era (and new dominant players grew, like Qlik and Tableau), allowing analysts to do some kind of analytics and data discovery, with less well-defined requirements and dynamic visual dashboards as outputs.

Gartner now states that we are in the time of the smart analytics and citizen data scientist era, with a focus on self-service data prep, automated analytics, and data recommendations and flexible visual data discovery. I humbly concur, but want to add that the present time also appears to be the optimization and prediction era, where focus is on improving existing operational workflows and investments with a strong impact on the bottom line by leveraging location data and machine learning (and we will see new players emerge like Alteryx, Rapidminer, or CARTO).

Location, which is so intimately tied to operational efficiency territories, capex investment evaluations, etc., is not just a nice market niche but a key distinguishing component for corporations to achieve sustainable competitive advantages. Businesses can start assessing product performance with greater precision, use big data to integrate disparate information on consumer expenditures or buying patterns, consumer creditworthiness, or even perform credit risk optimization.

The location differentiator strikes me as even more relevant, since market competition will only become fiercer as large business intelligence vendors add more capabilities to their products and new low cost platforms enter the market (like Google Data Studio and the newest cool kid on the block, PowerBI). As the market industry becomes saturated it will lead to a zero-sum commoditization in the visual discovery space, while opening opportunities for hot new tools such as self-service data preparation and location intelligence.

Location intelligence is still at its infancy and we certainly need more of those concrete use cases and compelling events to become mainstream. However, the beauty of location intelligence comes from the aggregation of different data sources into the same place, which enables the compiling of perspectives that display into a single unified view and give corporations an insight they didn’t have before. Making the invisible visible is one of my most beloved mottos.

Considering that it is inevitable that different tools are and will be used, seamless connectivity among platforms and a clear understanding of the value each one provides needs to be outlined. It is necessary to clearly define how each solution orbits in the planetary system of enterprise software tools ranging from big data discovery, to data preparation and ETL, up to advanced analytics, and predictive and location intelligence. Some vendors are huge gaseous giants, with a great amount of satellites attracted to their gravity (like SAP or IBM), while others are quicker and more flexible meteorites, able to go farther to unearth an insight, but without the same enterprise-ready depth.

CARTO is a crucial middle ground solution that can go from the analytics workbench used by information analysts to unearth insights, to a self service tool for data output that the rest of the organization can use to harness last minute analysis and collaboration, over shared datasets and applications. Where other organizations might keep these two distinctions in their product (a tool for analysts/data scientists or a tool for business users), CARTO provides a scalable and contextual visual analysis solution that facilitates the ease and execution of analysis and, very importantly, data storytelling.

Storytelling is necessary in the explanation of how analytics are applied, so that the entire organization is strategically aligned with the right information. How that story is delivered and focused around a compelling event, specifically relevant for someone within the organization is paramount. Whether it is illustrating an event you are influencing, the specific data relevance to a client, or defining which roles and business models are affected by a decision or demographic.

From a corporate perspective, considering this crowded landscape, the goal should be to minimize the number of vendors you really need, find what best fits your workflow, understand that this is a fluid process, and that new tools will appear as your needs evolve. This becomes a fun challenge of monitoring what is changing in the market and reaping the best ROI, without going crazy over needed skills and internal costs.

It should never be forgotten that value and ROI always boils down to, finding real actionable business outcomes. This typically happens many times at the last step of data sharing and collaboration. So the best tool is actually the set of tools that provide real business value.

Next year I am certain that the rapid evolution of location intelligence and data contextualization will shed new light on the position it holds in the market, what we do and what we don’t do, who are our much needed partners, and who are our competitors in a world of coopetition and continuously changing frenemies.

Exciting times ahead.

Miguel Arias

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