Invoice, the Critical Business Document, Explained [2020]

“Sales cures all” is a commonly accepted and mostly accurate piece of business wisdom. Sales does not translate to revenues if your company can not issue invoices. And your company can not deliver its services or products if it does not pay the invoices of its suppliers. Here, we explain all there is to know about invoices.

What is it?

Invoice is a document that summarizes relevant details of a sale so the buyer can pay the seller.

According to Wikipedia:

An invoicebill or tab is a document issued by a seller to a buyer, relating to a sale transaction and indicating the products, quantities, and agreed prices for products or services the seller had provided the buyer. Read more


AI in analytics: How AI is shaping analytics in 2020

I started my career as a management consultant. Excel was our temple for analytics. For a recent graduate, macros and connected models perform miracles but albeit at great effort. However, our reach was extremely limited compared to the possibilities of today. We could not process anything with images, text, audio or video easily as non-technical users. Fast forward to today, citizen data scientists are unleashing machine learning on all of companies data to run diagnostic, predictive and prescriptive analytics. Read more


Be a citizen data scientist: Tools democratizing data science

Analytics vendors and non-technical employess are democratizing data science. Organizations are looking at every employee as a data scientist so that they can bring their expertise on a business problem.

Most industry analysts are highlighting the increased role of citizen data scientists in organizations:

  • IDC big data analytics and AI research director Chwee Kan Chua mentions in an interview: “Lowering the barriers to allow even non-technical business users to be ‘data scientists’ is a great approach.”
  • According to Gartner, data scientists create models that use advanced diagnostic/predictive/prescriptive  analytics. Their primary job function is outside the field of statistics and analytics.

Why are there more citizen data scientists now?

These trends support democratization of analytics:

What are the tools used by citizen data scientists?

Citizen data scientists first need to access business data from various systems. For example, is a self-service data reporting tool which allows employees to pull data from various databases for easy analysis and automated reporting. We have listed and other solutions for reporting. Read more


State of Quantum Computing in 2020 for Business Leaders

Spare yourself the trouble and delay learning anything about quantum computing until 2020 eoy unless you are working on:

  • a problem that is not solvable in reasonable time with current computers (e.g. building deep artificial neural networks with millions of layers or simulating molecular interactions). Such problems are common and almost all Fortune 500 companies could benefit from quantum computers
  • Cryptography, or at an intelligence agency or need to transmit nation or mega corporation level secrets
  • quantum computing (sorry had to be MECE)

If you are in one of these fields, quantum computing has the possibility to transform your field in a few years. If not, check back in 2020 eoy, technology may have progressed to the point that others may also need to learn about quantum computing.

As non-technical corporate leader, what should I do about quantum computing?

If you are working on cryptography, or at an intelligence agency or need to transmit nation or mega corporation level secrets, stop relying on cryptographic methods that rely on factoring large integers. There are already quantum-ready alternatives as we discuss in the use cases section. Read more


Data-driven decision making in 2020: Step-by-step guide

Most successful organizations treat data-driven decision making as a primary objective and pursue it with religious zeal. However, data-driven decision making, the steps leading to it and how AI is changing it are not well-defined.

In an AI-first company, data-driven decision making means

  • Strategic decisions are made by a diverse group including executives that rely on a sufficiently comprehensive set of information. By definition, a bad strategic decision should be important enough to lead to failure of the company.
  • Most operational decisions are handled by continuously learning machine learning models which produce explainable decisions. Operational decisions are frequent (once a week or more frequent) and not critical (a single mistake is unlikely to lead to failure of the company).
  • Operational decisions which can not be automated with good accuracy are delegated to humans.
    • If data is lacking, opinion based decisions are made.
    • If data exists and has been analyzed, decision maker relies on analysis.
    • If data exists but is not analyzed yet, cost of analysis determines whether an opinion or data based decision will be made.

    Before settling on this framework for decision making for modern corporations, we need to identify how we can evaluate different decision making models. However, if you like you can directly skip to the sections that interest you: Read more