Scaling analytics within digital transformations — too much data, too little time — Let’s fix that with Knowi
Hi, I’m Nate! I’ve written this blog post to explain how Knowi simplifies analytics for everyone in the organization, not just those engineers that love getting down and dirty with data wrangling.
If you’re reading this — chances are I’ve already reached out and you may be looking for more info about what we’re doing at Knowi. I’m well aware that most people don’t like the idea of being sold to — I feel the same way. However, if you’ve run into the classic “we have too much data, what do?” problem, delays resulting from incompatible data formats or slowed down analytics objectives — this blog post may shed light on how to fix those problems.
As more sources of data come online, having a flexible analytics architecture will be vital to maintain the momentum of ongoing digital transformations. To truly harness all the complexities of different data sources, Knowi may be the wind behind your data sails.
Let’s get to the point — buzzwords, they’re everywhere. What are analytics? To boil it down, analytics refer to trends found in large sets of data. You probably know multiple people who analyze data, and may even be a Tableau maestro yourself. In a nutshell, analytics are statistics that drive decisions, and the quality of those decisions affects every business’s bottom-line.
Knowi makes a critical difference for the way analytics are understood within a business. The term “data-driven” (also buzzword) has become a sort of new wave tech-culture strategy focused on ingesting data and determining mission-critical decisions based on the results. The data-driven approach has begun reshaping the way many industry leaders interact with various levels across the business — finding competitive advantages through analysis of historical data, while offering a better real-time view of vital operations happening right now.
At the same time, the way data exists has proliferated. Everyone loves a sexy spreadsheet, but new cloud-based technologies and data storage formats have made it not so simple. Personally, I find data clusters fascinating, NoSQL databases like Mongo and Couchbase uniquely useful, and there’s some level of “protect the baby” for any physical data warehouse. As the digital age has progressed, so has the way that data is created, organized, and stored.
However, analytics softwares have not progressed in-step with data technologies. With the changing tide of information, analytics tools stay in a static position unable to integrate data from brand new sources. There’s a good chance you’ve seen this directly — analytics platforms like Tableau require teams of people to prepare data before analysis and decisions can be made. This delay has been the bane of many analytics and BI team’s that I’ve spoken to over the past 6 months. Although organizations recognize the critical nature of informed decision-making, existing analytics platforms force those orgs to meet their data ingestion requirements- this usually results in data being “wrangled” into simple excel-like SQL formats, or expensive, unnecessary “ETL” components clogging technology budgets.
This type of analytics architecture is inflexible — as newer data sources come online with the implementation of new applications, databases, and other touch-points across the digital enterprise, the ability to quickly analyze available data will qualitatively impact decisions made. With new data tech coming online bi-yearly, organizations looking to harness the digital transformation across different business domains will continue running into the dilemma of data disparity. Each new online touchpoint, database implemented, application designed, presents a challenge for analytics tools that are stuck 10 years in the past.
You might not of heard of us — we’re a SF Bay Area start-up playing an important role in digital transformation projects at a number of Fortune 50 companies. The reason is in the secret sauce: our analytics platform natively connects to any data source no matter what, without extensive set-up or training required. This causes massive disruptions in the analytics sphere. Most importantly, Knowi enables data analytics flexibility. This means that new sources of data can be immediately integrated into the analytics workflow. This is disruptive, because it turns a process that is currently a cost (preparing data for usage in Tableau) into a pro (immediate, in-depth analytics on new data sources). As a result, digital transformations can see analytics capabilities as a greater value-add, rather than a reactive cost-sink.
At massive scale, Knowi transforms the way analytics projects exist by enabling “Data Democracy”. In essence, this refers to the ability of all levels of an organization to access and harness data to make better decisions. In theory, this sounds simple — but the reality is more convoluted. As a result of digital transformations changing the way data exists, organizations have begun seeing an increased segregation of data from different departments. Sales and marketing may never have access to the product team’s data, because Mongo doesn’t fit well into Tableau. Knowi eliminates the barriers to Data Democracy by enabling all data to be used on the fly, in an easy to use way.
Knowi lowers the knowledge requirement to run complex analytics and solve difficult questions. We’ve done this using Natural Language Processing (NLP). This enables important data to be analyzed using simplified English sentences. Imagine relevant teams being able to find information from anywhere in the enterprise’s data collection, using phrases like “how many sales last quarter?” or “how many devices did plant X produce in July 2015?”. I know these aren’t the most creative examples, but the magnitude of potential discovery this unlocks cannot be overstated.
By natively connecting to any data source, Knowi enables businesses to harness all available information to make better-informed decisions. The end result is a more effective data-driven organization, as analytics decisions are no longer concentrated in the hands of those capable of understanding specific platforms or limited by the format of data itself. Knowi was made for usage by anybody in an organization with countless ways to deploy, analyze, and use data inside a single “off-the-shelf” software. Data scientists can begin incorporating more real-time information into machine-learning algorithms, marketing teams can pull important statistics from difficult to access internal ERPS, CRM’s, or databases. This democratization of data removes one of the main roadblocks to digital transformations — implementation across all levels of the business. With customizable ways to put important metrics in the hands of end-users, while offering them the ability to find their own insights — Knowi supercharges your team’s digital transformation with scalable analytics.
If you’ve read this far, you might have a few questions about what makes Knowi so special — feel free to send me a message (firstname.lastname@example.org) and I’ll try my best to respond promptly. I’m always happy to chat about ongoing projects, NFL drama, or data & analytics in general.