Modern organizations can outrun their competitions by enabling their data biz operators to help make data-intensive operational decisions with the ease of spreadsheets.

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Business Operators. Operations in organizations are run by humans: tracking accounts payables and receivables, managing inventory in an e-commerce store and manufacturing, auditing insurance claims, managing KYC, patient care in hospitals and nursing homes, product marketing and more.

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Physical to Digital. With the acceleration of “everything-as-a-service” eating every industry, the traditional reliance of such business operators on “physical” observations has been increasingly replaced by “digital” sensing [no wonder, businesses continued operating in COVID]: retail is moving to e-commerce, health care to telemedicine, cash to digital currency, and more.

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Rise of Data Operators. This physical to digital transformation has led to the rise of business “data operators”: individuals in organizations, from the ground level to the C-suite, who are using data as their primary “sensors” to run mission-critical operations.

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With business operations now encoded as data, data operators will compose and orchestrate “atoms”-based infrastructure and processes as we do with APIs: anyone will be able to build, manage, run, and scale entire supply chains, banks, or hospitals from anywhere, without code or worrying about physical infrastructure.

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The Opportunity. There are more than 30M data biz operators running mission-critical business operations using data. All of them experience this pain across functions such as accounts payables, accounts receivables, inventory management, claims auditing, and more. Organizations typically address it by throwing talented human resources at it. As a result, the process remains slow and error-prone at the best and leading to damaging mission-critical business decisions at the worst. As the global competition becomes stiff, the organizations that will be better equipped at helping their data biz operators will come out winners.

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The Pain. However, these users are still primarily stuck managing mission-critical business operations in spreadsheets today. This is because, as physical operations are digitized, data operators are plagued with the typical problems of working with data.

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Today, the typical workflow of a data operator involves (a) accessing (structured and unstructured) data from multiple first-party and third-party sources, (b) stitching them together to produce a ground-truth, (c) inspecting, analyzing and collaboratively evaluating operational choices, (d) making decisions, operationalizing them and evaluating prior decisions.

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For example, a supply chain or inventory manager in e-commerce would extract the status of inventory from multiple portals daily, stitch them manually as the schemas do not match; use the insights, instincts or help of the data science team to decide how much to order to satisfy future demand. Similarly, a claims auditor in insurance would be required to obtain information about a claim from multiple sources to manually stitch and verify it. For accounts receivable, invoices and payments through different sources need to be stitched manually; the manufacturing orders and received shipments need to be continually matched to keep track of the supply chain; etc.

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The pain of such data biz operators is further exacerbated by the fact that data is scaling, and outcomes from such data processing need to be utilized for downstream analysis and collaborative decision making.

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In-adequate Landscape of Software Tools. There has been exciting progress over the past decade towards bridging this gap, but it has not been enough. However, tools have either focused on data and analytics or operations, leaving a gap to fill.

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Despite such importance, the go-to software tool for data operators is primarily the spreadsheet. To understand why, let’s take a quick overview of the landscape.

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Traditional analytics tools (BI, data warehouse, notebooks) are meant for ad-hoc, low-frequency analysis, not high frequency, daily operations. Unlike traditional data tools that have been built for analytics such a tool (for data operators) must, without code, enable non-technical operators to: (a) work with messy, structured and unstructured data sources, and (b) build, manage, and monitor various “human in the loop” automations and inputs.

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Further, unlike traditional tools built for automation (RPA) or running operations (no/low code platforms), such a tool must enable non-technical operators to use analytics to make informed data-driven decisions as “humans in the loop”.

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RPA is the most popular no / low-code approach to automating processes and is one of the fastest-growing market segments (33% CAGR), with UiPath (the market leader) growing from $8MM to $200MM ARR in just 2 years and IPO’ing earlier this year at a $38B market cap (one of the biggest IPOs in US history).

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Despite this success, current RPA approaches can only automate about 20% of business processes and are still very difficult to implement.

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In Summary. We have a great toolset available for data engineers, data scientists and data analysts, but not data operators.

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To fill this gap, such a tool must enable data operators to (1) build and automate complex processes from data, and (2) run complex processes.

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Enter Ikigai: Bridging the gap between data and operations. Ikigai is building the canonical cloud-based, collaborative platform for data operators to overcome these challenges, and empower them to “program and run the business”.

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Ikigai allows data operators to build powerful end-to-end workflows with their spreadsheet skills. Equipped with AI behind the scenes, data biz operators can, within clicks:

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1. Extract data from disparate structured and unstructured data sources and automatically stitch them without needing to manually clean or prep data.
2. Build, manage, and monitor various “human in the loop” automations, without coding, e.g. it will enable complex workflows such as reconciling and triaging data errors seamlessly.
3. Go beyond dashboards — receive AI-driven recommendations, simulate “what-if” scenarios, collaboratively make decisions and operationalize them in downstream systems seamlessly through in-built connectors.

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In short, with Ikigai’s AI-charged spreadsheets data biz operators have found their Ikigai (the passion and reason to live)!

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About the Authors

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Vinayak Ramesh (Co-founder and CEO)

Vinayak previously co-founded Wellframe ($45MM+ funding to date) to help leading health plans utilize A.I. to manage their complex patient populations.

He is an MIT-trained computer scientist and was selected to the Forbes 30 under 30 list in recognition of his entrepreneurial work. He received his S.B. and M. Eng degrees from MIT.

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Devavrat Shah (Co-founder and CTO)

Devavrat is a Professor of AI+Decisions within the department of EECS at MIT. He is the founding director of the Statistics and Data Science Center at MIT.

He previously co-founded Celect ($35MM+ in funding, acquired by Nike in 2019) to help leading retailers utilize A.I. to optimize their inventories. He has made seminal contributions to statistical inference and machine learning that have had an impact in academia and industry.

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Originally published on medium.com