We just launched the alpha-version of our point-in-time historical brand M&A data base and API. Submit a product code and date the API will return ISIN code (if public) of the owner on that date.
The objective behind the redesign is to make better use of screen real estate, to ease navigation and simplify work flows. Since we began development, the product has become more complex, by necessity. Making it simple and easy to use is central to the brief.
The rolling brief of “simplify” will continue to be used as the capabilities of the platform become more advanced. The UI will continue to evolve as more features are launched. In this release we have added the following features:
Data formats – users can now import zipped files, comma- , semicolon-, and pipe-delimited data files structures. For parsing we now have automatic detection of delimiters.
Column Reduction – users can use this feature to delete fields in the data and save a new, reduced, version of the data. This is a useful feature for stripping out PII fields or fields that contain “bloat”. Improving performance and enhancing security.
Data Extraction – users can extract unique lists of values from fields in a data set. The primary use case for this feature is to allow users to create audiences based on behaviors. These audiences can then be appended to new data sets to identify cross-over behavior.
Data Sampling – users can randomly sample rows from a data file. For very large data sets, performing exhaustive calculations is time and resource intensive. Sampling a data set and analyzing a subset is based on sound statistical principles and rapidly increases productivity for large data sets.
Transform Filters – users can transform a filter in to a mapping file. Data reduction is an important step in data analysis, converting filters into data reduction maps will make this effortless.
Dynamic Mapping – users can access API end points, pass values to the end point and take the returned value as the “mapped value”. Initially this will be limited to an internal api that maps product code to brand and owner. New API connections will be added over time.
Multiple AWS Accounts – users can now specify multiple AWS account access keys to connect to. This is to incorporate the launch of KL data products. KL now offers a range data products that firms can subscribe to. Multiple AWS account capabilities allows for customers to bring many different data streams into the account environment on the platform.
As well as building solutions that can be accessed through a simple form/button led UI, these features are the building-blocks of future analytics solutions. These features are be platform-wide universal tools, untethered from a specific context or environment. This will give our product development team greater flexibility to design and implement new functions and features.
While keeping people in silos is a good thing for managing and directing them, it tends to be bad for business in the long run. Especially for businesses that rely on innovation for growth.
In the book, The Medici Effect, the author describes how the wealthy 14th century house of Medici created the conditions that led to the Renaissance – a period when there was an explosion of ideas across the arts and sciences. This was only possible because the family’s wealth was able to support artists from different disciplines who shared ideas, a lesson to companies that want to innovate.
What’s true of people is also true of data. Not all data is created equally. As a result it tends to be put in silos determined by source (transactions, surveys, crm, etc). Different data has different degrees of meaningfulness; transaction data tends to be narrow but very deep (telling you a lot about a very narrow field) whereas survey data tends to be broad but less deep. Combining data with different strengths can uncover new insights. Linking transaction data with survey data can identify broader behavior drivers, these can drive sales and increase customer engagement.
In our mind, silos are bad for data too. They prevent data owners from making new discoveries that arise from merging a customer’s data.
Knowledge Leaps de-silos your data, creating a single-customer view. Allowing companies to look at the drivers, interactions and relationships across different types of data, whether its transactions, surveys or CRM data.