The world is changing at the speed of code, and as more companies adopt more and more technologies to run their business, data is becoming the lifeblood. We now live in a world where everyone is an IT company in some form. It used to be that you were a bakery now you live on a dozen SaaS applications. It used to be that you were a hospital, and now you are living on 100’s of cloud and SaaS products. Data is becoming more disparate and scattered. Organizations are faced with limited resources, including time, money, and human capital. With current data analytics solutions on the market built only for those with capital and technical skill sets, many other companies in the world struggle to get access to their data in an affordable, simple to use, and approachable manner. 

A recent survey by CSO online found that on average companies are using 80 separate SaaS applications to support business functions and store sensitive data. Organizations need to keep up and must have an easy way to make sense of all the data populated from the ever-increasing amount of applications and tools. This is why at Fletch we are focused on bringing to life ready-to-use analytics. So let's start with first getting into what ready-to-use analytics are?

What are ready-to-use analytics?

Unlike data analytics platforms that start out empty and leave it to the user to figure out all of the data plumbing, upkeep, and visualization, ready-to-use analytics does all of the hard work for you. Ready-to-use analytics are:

• Bite-sized, easily understood by everyone in the org, and specific to business needs 
• Turn-key: automate the entire data plumbing process 
• Instilled with expert knowledge and automatically surface critical insights 
• Cost-effective 

With ready-to-use analytics, the amount of time it takes to get started is dramatically shortened from months to minutes, eliminating the busy work so you can quickly gain visibility into your environment. Business operators can get right to results by automatically connecting and maintaining data feeds to know where they stand when it comes to risk posture, compliance frameworks, workforce risky behavior, and major threats emerging every day in the news.

With Fletch's ready-to-use analytics, it takes 15 minutes to connect your data, then Fletch automatically indexes, normalizes, and triangulates your logs, metrics, and metadata in hours. Within 24 hours, Fletch surfaces actionable insights vetted by top-industry experts on day one, so you can get answers today and not months from now. With Fletch, you can buy a single solution at a time and connect the integrations that are important to you instead of having to purchase an entire platform that you may end up using a small portion of. We're bringing one solution to life at a time and are starting with addressing top visibility issues for Security and IT leaders, like:

• Monitoring user behavior to mitigate the loss of high-value assets
• Continuously monitor cloud controls to expedite SOC2 compliance
• Monitoring emerging threats and how they impact your environment

Ready-to-use analytics empowers operators, leaders, analysts, the technical and the non-technical in every organization by making data approachable and easy to understand, and the world needs ready-to-use analytics now more than ever. 

Why are ready-to-use analytics so important right now?

As digital transformation is ever-increasing, data is not only disparate; it is scattered everywhere. As a multi-time founder of three ventures and currently on my second venture focused on data and cybersecurity, I witnessed the emergence of a new era ten years ago, The Big Data Era. Many promises were made during the big data era, such as data could be harnessed, analytics could be derived from everything, and yet we are all still waiting on these promises to be fulfilled. 

To be fair, a small number of companies have been successful with harnessing and deriving analytics from their data, but not every company can be a Facebook or a Google with the means to hire the best (and pay the most) to build out these big data tools. The ambitious companies that marched forward during this era were met with, and are still facing, many challenges of harnessing big data to derive meaningful and actionable insights.

The Challenges of Data

Data is everywhere, and this is especially true as the world moves more into the cloud. As companies begin this data-plumbing journey, they hire the best people to get their data in shape. Still, with the talent shortage surrounding data analytics, the demand for data-literate professionals is now extending beyond traditional data science roles, putting a tighter strain on companies to instead hire consulting companies that promise the "land of data opportunity." Consultants push companies to hoard all of their data, creating a data lake full of expensive, unstructured, raw data that eventually turns into an even more costly data swamp if not properly designed and maintained. 

Suppose you are fortunate enough to have found a consultant to harness your data: in that case, you most likely are spending a few hundred dollars per consulting hour, and are stuck with a couple of static dashboards that have to continually be updated, exo-facto more consulting hours, and not to mention, any changes you want to make to your cloud providers potentially means starting the project again from the ground up. 

If you are even more fortunate to have recruited a data expert to join your team, you are still facing challenges in your data-plumbing journey. The current solutions available on the market are costly and are essentially empty shells that require a high level of technical resources to tool out. This results in your data expert that you spent many recruiting cycles on to now be burdened with weeks, sometimes months on data plumbing and data modeling before getting real value. All yet to still be faced with ongoing maintenance projects. With the national average of a data scientist's salary in 2021 being close to $115,000/annually, it's only a matter of time before this highly paid employee becomes frustrated and understandably so because they were hired to uncover and extrapolate analytical insights, not be a data plumber. 

The Cold Start Problem

Whether you hire a consultant or a data scientist to do the data plumbing, the cold start problem is an issue that everyone needs help with. The cold start problem refers to when you are finally starting to extract analytics from your data, but you don't know what to ask of your data or where to begin. This can lead to a massive game of telephone because the person who has the vision is not technically skilled to get the insights. It becomes a constant back and forth between the data scientist and the visionary asking questions, hoping that the questions being asked are actually the right questions being answered. In rare cases, a consultant or a data scientist may be a "unicorn" who knows how to plumb and model your data and knows what to ask of your data to get the key operational insights needed to run the business. 

Beginning with the big data era that's still withholding its promises, to the labor shortage of skilled data scientists, the highly-paid data consultants, the expensive data lakes and swamps, to the months spent on frustrating data plumbing and data modeling, to only reach a cold-start of not knowing what questions to ask, it's easy to see why we need something better now more than ever. 

Digital Transformation For Security

As businesses move away from physical, on-prem data centers to the much more flexible, efficient, time-saving, and cost-effective cloud, the journey can be complicated. In the traditional on-prem setup, typically, a data center manager is the point of contact for all things data, from the infrastructure design to managing and understanding how resources are allocated and physical security. 

Flip to the scenario of data stored in the cloud and managing the items mentioned above, and it becomes much more difficult to control. In a cloud environment, engineers can now spin up instances that unknowingly increase your cloud bill or accidentally open an S3 bucket with personal identifying information (PII). Actions within the cloud environment are much more difficult to track. This is where ready-to-use analytics becomes paramount for the journey of harnessing data within the cloud.

Ready-to-use analytics are built to be accessible, easily understood, and affordable, empowering companies of all sizes to get the most out of their data. As organizations continue to face limited resources, it is not practical or efficient to devote months to modeling and plumbing data before getting insights with traditional analytics platforms. With ready-to-use analytics, organizations now have the opportunity that previously only the largest and most resourced companies had, but at a fraction of the cost and the time. 

If your organization is just beginning to move to the cloud or has too much data to even know where to begin, or if you're stuck in a never-ending cycle of getting your data in shape, ready-to-use analytics can provide meaningful and actionable insights within 24 hours. The vision at Fletch is to finally deliver the promise of harnessing data, regardless of where you are on your data journey, your company size or maturity, and regardless of technical expertise. We're just getting started and are currently in closed beta, if you want to know more, sign up here or email us at beta@fletch.ai.