We rolled out our No Code Database feature today. Just plug in a data feed and add data to a customizable database with zero lines of code, and zero knowledge of the inner workings of databases. All this in under a minute.
Setting up a database in the cloud is confusing and complex for most people. Our technology puts the power of cloud-based databases at everyone’s finger tips. No need for the IT team’s intervention. No need to learn remote login protocols. No need to learn any code.
We have also added in some useful aggregation and summarization tools that let you extract data from databases straight into reports and charts. Again, no code required.
This is the valuing of your own labor rate that takes place after your third or fourth trip back from Ikea. You know you have saved some money buying a set of shelves that you need to assemble but part of the decision was made by assessing how much time it would take to put the item together vs how much you saved.
Ikea is in the business in devaluing our self-perceived labor rate so that they can charge the most for a flatpack item such that the discount achieved justifies the hours needed to assemble the item. (This is the same model used by meal-kit businesses, or at least it should be.)
For items that do not require assembly then the trade-off people have to make is their willingness to buy products that do not comply with the standards for that type of product bought everywhere else. For example, a desk lamp from Ikea requires no assembly so there is no time-cost to save. To justify the lower price there must be an investment from the customer. In this case it is the willingness to accept a non-standard shade fitting. The same is true of other non-assembly products sold at Ikea.
For those folks interested in alternative data, there could be a macro signal regarding wage earnings and wage growth buried in this data. Comparing the price of an Ikea product with a similar (assembled) non-Ikea product over time could be a useful economic indicator.
We have upgraded our online user guide. We have added pages on new functionality and pages on some use cases for the platform. It is a living document so it will change regularly over the next days, weeks and months as we add new content to it. Here is a link to the site. Enjoy!
We are adding to our no-code data engineering use cases. Our new CollectionManager feature plugs data pipelines into databases with no code just using a simple drag-and-drop interface.
This feature allows users with zero knowledge of databases and query languages to import data into a database. An additional UI will then allow them to create queries, aggregations and extracts using a simple UI.
The UI can be set up to update the database with new data as it is arrives from external sources, it will also automate extract creation as new data is added.
Example use-cases for this feature would be in the creation of data feeds for dashboards that auto-populate, or creating custom data products which can be timed with a guaranteed delayed delivery time. This feature will also drive our retail experimentation business – we can design and set up a data framework that captures and tags the results from test-and-learn activity.
It doesn’t take long to realize that data is agnostic to source. Web logs look similar to retail transaction data. Survey data and customer profile data can be handled in the same way.
When building a data platform, finding a niche is hard because if data is source-agnostic, so is the platform. The platform we have built is a generalist product; point it at any data source or stream and it will be useful. What links all the data sources together is another generic concept – customers.
Generalist products are hard to sell, primarily because it is hard to find audience insights that help contextualize the problems the product can solve.
For the past five years of building the product we have found it is easier to write code than create a succinct product proposition. In recent weeks, some ideas have been crystalizing and we landed on this:
Knowledge Leaps is a customer data platform for storage, engineering, and analytics of all types of customer data.
Knowledge Leaps is a cloud-based data management platform that allows for collaborative analytics, data management and data-workflow management.
There will always be a plentiful supply of data scientists on-hand to perform hand-cut custom data science. For what most businesses requirements, the typical data scientist is over-skilled. Only other data scientists can understand their work and, importantly, only other data scientists can check their work.
What businesses require for most tasks are people with the data-engineering skills of data scientists and not necessarily their statistical skills or their understanding of a scientific-method of analysis.
Data engineering on a big scale is fraught with challenges. While Excel and Google Sheets can handle relatively large (~1mn row) datasets there is not really a similar software solution that allows easy visualization and manipulation of larger data sets. NoSQL / SQL-databases are required for super-scale data engineering, but this requires skills of the super-user. As ‘data-is-the-new-oil’ mantra makes its way into businesses, people will become exposed to a growing number datasets that are beyond the realm of the software available to them and, potentially, their skill sets.
At Knowledge Leaps we are building a platform solution for this future audience and these future use-cases.The core of the platform are two important features: Visual Data Engineering pipelines and Code-Free Data Science.
The applications of these features are endless; from building a customer data lake, or building a custom-data-pipeline for report generation or even creating simple-to-evaluate predictive models.
Benford’s law is how the IRS/HMRC can tell if the information you submit on your tax filings is fraudulent. When people lie on their tax forms they tend to use random numbers, when really the number distribution should follow Benford’s law. https://en.wikipedia.org/wiki/Benford%27s_law
On recent visit to Southwest Utah I saw lots of pygmy forests containing pinyon pines and small oak trees, these forests are sparse and the trees no more than 8-10 feet tall. The National Park literature says that these trees have adapted to low water conditions. Contrast this with the Redwood forests of coastal California where resources (water & sunlight) are abundant. In this environment the trees are more densely packed and grow much taller.
Replace trees with firms and resources for customers, and this paragraph could describe a business landscape. Being binary for a moment, a new firm gets to choose between choosing to enter a market where resources (customers) are slim or to enter a market where there are lots of customers. Choosing a market with few customers, makes it easier to differentiate your firm but the odds of survival are worse. Choosing a market with more customers makes it harder differentiate your firm and therefore the survival odds are also tough.
Unless of course, your firm is first. In both instances you get to choose the best position and consume all available resources.