API economy has already established itself as a precursor of digital transformations and the primary way to grow an ecosystem.

“API monetisation and API first strategies have become a new normal with businesses with digital maturity.”

Last year’s pandemic catalysed digital maturity across organisations. The niche markets found even more niche business opportunities, thanks to the widespread adoption and development of APIs (Application Programming Interface).  In their most basic form, APIs are doorways between two software applications and become extremely powerful when tailored to the needs of the developers. Web, mobile and automation are some of the key applications powered by APIs. According to a report by Google Cloud, API programs are the core drivers of digital transformation by playing a significant role in digital experiences, business operations, innovation, and growth. 

Companies around the world possess valuable data ready to be capitalised. All they need are the services that can bridge the gap between customers and third parties. APIs fit right into this mix. For instance, the banking sector has witnessed a tremendous revolution with the advent of fintech products. The infrastructure behind the payment gateways are powered by the APIs like those of Stripe or Razorpay. These fintech API providers are multi-billion dollar companies today. Machine learning-based API service providers are next in line to take the markets by storm.

“Databricks API  supports services to manage clusters, instance pools, libraries, tokens, and MLflow models. Databricks is currently valued at $28 billion.”

For example, last week, Databricks, a company that offers unified platform services raised $1 billion that rocketed its market value to $28 billion. Though AWS too offers Spark services, Databricks’ Spark services seem to have an edge over them. They offer additional customisations while combining the synergies of top players to serve an user.  

According to an Apigee survey, AI- and ML-powered API security and monitoring solutions used for anomaly detection and security analytics grew 230% year-over-year between September 2019 and September 2020.

When easily reusable, APIs let developers modularly combine, and recombine functionality and data for new uses, with virtually no marginal cost for each additional use of the API. If one developer builds a new application by leveraging an API that looks up store locations, another developer can leverage the same API for another application without the enterprise incurring any additional overhead.

The APIs will (source: Gartner):

• Make it easier for data scientists to find and choose from the huge variety of available algorithms, experiments, datasets and solution accelerators.
• Enable organisations to build advanced analytics solutions in a faster way.
• Address the skill gap in advanced technology.
• Help commercialise their solutions easily.
• Enables ease of ML model choosing.

APIs also allow the organisations to take smart decisions by providing details of the product consumption at the user level, which in turn can be used by the developers to enhance the end product. This sounds like every other business strategy, but APIs make it more accessible. It helps them understand the value of an organisation’s digital assets. Beyond helping enterprises, writes Bala Kasiviswanathan of Google Cloud, API analytics can help both IT and business leaders refine the KPIs they use for analytics. “If an API becomes popular with developers in a new vertical for example, that may persuade the enterprise to focus on KPIs like adoption among these specific developers, rather than on overall adoption,” said Bala.

AI Through API

In 2019, machine learning as a service (MLaaS) raked in an estimated $1 billion and is expected to grow to $8.4 billion by the end of 2025. The success of these services can be traced to the customised APIs. For example, Google’s prediction API, can be used to classify an image for $0.0015 and even perform sentiment analysis on text for just $0.00025 only. The user gets to avail Google’s state-of-the-art tech and Google gets compensated for its research. APIs can act as conduit between innovation and incentives.

No matter what kind of machine learning product you are building, it eventually boils down to whether the customer can deploy these models with just a few clicks. APIs help do this. Research labs like OpenAI resorted to releasing APIs to commercialise their exotic research. The much talked about language model, GPT-3 was tapped through these APIs and was leveraged to set up many million dollar startups. Now, customers can access state-of-the-art ML models without the headaches of training from scratch; GPT-3 training that cost OpenAI over $4 million.

If you are an API service provider, then here are a few takeaways from OpenAI’s success:

• The team at OpenAI made sure their API is built to be simple and flexible.
• OpenAI would terminate API access if the users use it for applications such as harassment, spam, radicalisation, or astroturfing. AI, unlike other SaaS domains, can find malicious players easily (think: someone using GPT-3 to write fake speeches for Presidents that can start a war).
• For AI research to survive, there needs to be a commercial twist and APIs sit at the heart of this strategy.

Going forward, more Cloud and AI  based services will be offered as API-centric services. Services like AWS Lambda are designed for producing exclusively API/event-centric application services. According to Gartner, adoption of API-centric models for SaaS delivery is expected to increase and the API economy has already established itself as a precursor of digital transformations and the primary way to grow an ecosystem. 

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Analyttica Datalab is a technology-enabled analytical solutions company that drives business impact through a strong focus on its customers. The company offers an optimal man-machine blend of solutions that combine analytical expertise and technology and can repeatably be used by enterprises at significantly lower cost.

The company was founded by Rajeev Baphna, a veteran in the data and technology space, having worked with Citigroup for 16 years. Analyttica is basically around platforms, tools, technologies, and people. There are certain challenges in the tech ecosystem in relation to tools, technology and people, which led to Analyttica being founded. The first challenge is when you look at Big Data, AI and ML, there’s no holistic way to be able to define a business problem, solve it, extract the solution and run it at scale in a business. These components are addressed by 10-15 different tools and technologies. The second challenge is the ability for a business’s ROI to be visible right away. And the third challenge is talent, as the way people use these technologies and tools are still very process-oriented, and the ability to apply in a business context is still left to a human being. Analyttica was formed to be able to create solutions to these problems. They have also patented their approach in the United States.

The organisation has two primary products they take pride in. These products are built around the ‘learn, apply, and solve’ concepts. Analyttica TreasureHunt® (ATH) SimuLab is used by enterprises to create a long-term and sustainable data culture. Analyttica TreasureHunt® (ATH) Precision creates value in terms of solving the business problems highly rapidly and with 100 percent accuracy.

The ATH SimuLab product helps organisations scale and become AI-ready faster. It’s not restrictive in nature. The learners or the talent focuses on applying themselves contextually within an environment that simulates real life scenarios and do not need to be distracted by the coding aspects while doing so.

With ATH Precision, they are trying to develop a holistic analytical ecosystem for organisations, where they can connect to their data and different applications and contextualise their business problems, instead of just randomly running some AI or ML algorithms. As a team, they can collaborate and are able to solve the problems. They can institutionalise their learning and the experiences they gain along the way.

The organisation believes that you need to have a strong blend of subject matter expertise, business analytics exposure and technology, as well as a culture that fosters collaboration.

Their workplace has an agile design, so that everybody is accessible to each other all the time and they frequently huddle together to solve problems.

Developing the product was not a cakewalk for them initially on a technology perspective, but things slowly improved. They base ATH as a set of microservices and they have Docker-based deployment. They’ve also leveraged a lot of mature open source libraries and tools. The incremental and iterative approach helped the team to accelerate their learning faster.

The hierarchy at Analyttica is pretty flat and stands apart from other data and analytics companies. Most of their employees previously worked in bigger organisations with expansive processes, which also came with silos and boundaries. At Analyttica, those boundaries don’t exist, and individuals get exposure to diverse fields. They follow the vision to learn, apply and solve internally as well. Almost all meetings are an open door. Those who are hungry to learn and grow are given access to more challenges that they can help solve. Even amidst all the impactful work they do, Analyttica is able to maintain a fun-loving atmosphere, with games and potlucks, and the team is like a large family.

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Data is everywhere, but are we using It correctly?

Big data is an ever-evolving and dynamic term that describes large volumes of data with the potential to deliver useful insights and information. Big data can inform machine learning strategies, form the basis of artificial intelligence applications, and transform business operations.

For years, Big Data was defined by the 3 V’s. Companies looked at the extreme volume of the data we collected, the variety of data available, and the velocity required for data processing. These concepts were identified in 2001 by Gartner analyst Doug Laney.

Since then, various companies have implemented their own “V’s” into the big data discussion too, such as “value” and “veracity.” In other words – how valuable is your data, and how much can you rely on it?

As new technologies make data more accessible, how is the big data environment changing, and what does it mean to the future of communications?

1. What is Big Data

Research shows that 80% of the world’s data is dark. This means the information has never been used to drive business decisions. For years, the world struggled to access endless forms of information, all the way from the analytics stored in customer voice conversations, to the data in images.

Today, we’re discovering new ways to collect and analyse data from almost every business touchpoint. The result is that companies can dive deeper into a range of experiences. For instance, data obtained from a workforce optimisation tool shows you where your employees are their most productive, and where they need help to boost efficiency. Data about your CCaaS strategy can show you where you have gaps in your contact centre environment, and where it may be worth building extra channels into your omnichannel environment.

Big data analytics can even help organisations to get a better sense of their customers, and the journeys they take when making a purchase. With data, you can track down all of the touchpoints where your clients interact with your business and look for ways to improve their experiences. For instance, if you find that your audience prefer SMS contact to phone conversations, you can implement an SMS strategy to update them on their order progress or shipping status.

Big data analysis can also tell you more about individual customers so that you might provide more personalised up-selling suggestions or guide them towards products that are relevant to them.

The challenge today is in accessing data, without crossing privacy and compliance boundaries. As consumers become more concerned about how their private information is used, national regulations like GDPR have come into play. These issues force companies to think more carefully about the data that they can collect, and the kind of consent they must get from clients. Businesses can’t just collect data mindlessly. Information must be gathered with a specific strategy, purpose, and a high level of consent.

1. Big Data Trends

MarketWatch suggests that the global big data market will reach a value of $118.52 billion by 2022.Developments in the way that we can collect and store data, along with the ever-more flexible support of the cloud has helped the big data environment to evolve. All the while, we’re seeing a number of impressive new trends appear in the market, such as:

1. 1.      The Rise of Open Source Processing

Open Source applications like Spark and Hadoop continue to be crucial components in the big data space. Surveys suggest that 60% of enterprises expect to have open source clusters running by the end of 2019. Many companies are looking to expand their use of such technologies for data processing purposes.

As organisations continue to experiment with data mining tools, the focus will be on finding solutions that allow for the quick collection of useful information. However, enterprises will also have to be careful that their tools come with compliance in mind. Business leaders need to be able to find, access and manage the data in their stores when necessary too.

1. 2.      Edge computing and analytics

The demand for Edge computing is on the rise. Edge computing brings companies as close to endpoints and sensors as possible, to reduce traffic and latency in a range of networks. Gartner suggests that edge computing and cloud computing models will continue to evolve and complement each other in this year, and the years ahead. Cloud services may expand to live both in centralized servers and distributed on-premise servers and edge devices too.

Some people believe that edge computing and analytics will help to increase security and compliance in the business environment, due to their decentralised structure too.

1. 3.      Predictive Analytics

One of the biggest benefits of big data comes from its ability to inform machine learning strategies. Predictive analytics is a solution born from machine learning. By gathering huge amounts of historical data, companies can predict everything from when a machine on an industrial floor needs replacing, to when customers may begin to churn.

Predictive analysis can offer companies of all shapes and sizes insights into what they can do to transform their business environment. Through predictive analysis, contact centres can even ensure that they’re prepared for changes in consumer trends, and influxes in calls.

4.      IoT

The rise of the Internet of Things is set to have a significant impact on the big data landscape. Gartner predicts that there will be 20.4 billion connected IoT devices by 2020. As such, the volume of data companies will be able to collect will grow dramatically. Organisations will need to implement new systems and technologies to handle the flood of information coming into their business.

Business leaders that can respond well to the IoT environment could discover incredible insights about how their products and services are used, or even how the industry overall is evolving.

Big Data Statistics

The market for big data is growing every second. We’re continually creating new information. For instance, the volume of data produced by companies in the US alone is enough to fill more than 10,000 libraries. Here are some of the facts you need to know about big data:

• Data-driven organisations are 23 times more likely to get customers, 19 times more likely to be profitable, and 6 times more likely to retain clients.
• 65% of client-side marketers say that improving data analysis is key to understanding customer experience requirements.
• US decision-makers say that big data analytics is the most important technology for improving customer experience.
• Most companies only analyse up to 12% of their available
• Bad data costs the US economy $3.1 trillion each year
• Only 48% of businesses say they already have data analytics systems in place
• Worldwide marketers say that their biggest challenge in driving a customer-experience strategy is fragmented data systems.
• 49% of companies say that machine learning is crucial to helping them process customer data, provide real-time analytics and develop better sales models.

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It is no surprise that data science today has become the backbone of several organisations across the globe. From the core to the edge, a lot of organisational decisions are based on data science. With time the job role of a data scientist is just becoming wider and talks about T-shaped data scientists is gaining ground.

However, even though the domain has experienced a major boom, it is fraught with its own unique set of challenges, and now, it is experiencing a shortage of top talent for specific job roles such as Data Science Manager. But why is it happening? To answer this question, we will have a look at the reasons why Data Science domain is witnessing a talent crunch for DS managerial roles and how one can become a DS manager.

The Need For Data Science Managers

A lot of students are taking up data science courses to start their career. That is not all, a lot of professionals from other domains are also making the transition and taking up data science as the career path. And why no — it is one of the highest paying jobs.

But now this boom is posing a huge challenge to a lot of companies across the world. While the number of data science professionals is increasing, the number of managers are still the same. The companies were not realizing this lately, but now they are actively looking to onboard DS managers.

Having a strong data science team is one thing, but if you don’t have a manager who is goal-oriented, cares for the team, actively listens to them for making decisions, is a mentor, and empowers and inspires team members, then the efficiency of a data science team decreases.

Now you must be wondering that if it only takes leadership skills then why companies are not hiring managers from other domain? The reason is that a DS manager along with leadership skills need to have advanced knowledge of data science — if s/he lacks that knowledge how they are supposed to lead a team of data and analytics professionals and implement data science solutions?

In order to cope with these challenges, many organisations have started training and promoting the leading data scientists to managerial roles. And it is turning out to be one effective solution. So, if you are also planning to make a transition, then along with all your data science skills and knowledge you need some of the other skills.

Here Is How One Can Make A Transition Into DS Management Role

1) Build A Know-How Of The Data Science Domain

One cannot be a leader without knowledge of the field, and data science is no exception. To be a data science manager, you need to have in-depth knowledge of the data science domain and a significant amount of experience, as you would be leading a data science team with people having different skills. If you are working as a data scientist and lack certain skills and concepts, make sure you seek help from your fellow data scientist.

2) One Cannot Jump Directly To A Managerial Post. Get promoted, First!

This is something you all have to accept that one cannot directly make a transition into a managerial role — there are certain criteria that need to be followed. And one of the criteria is to get promoted to posts such as IC, lead data scientist etc. This would give you a platform to perform for a bigger picture.

When your work gets recognized, you get promoted and that means, you are one step closer to your goal of becoming a manager.

Word to the wise: Even though you get promoted to a different role, do not stop your data science learning process.

3) Start Developing Leadership and Mentoring Skills

When reaching a certain position as a data science professional and gain a significant amount of knowledge, try to help fellow data scientists or the juniors. There is nothing wrong in proactively asking your juniors if they require any help in solving critical data science problems. This might require extra hours, but considering the perk that is completely okay. The more you share your knowledge the more you gain.

If you are a lead data scientist, make sure you correct your fellow employees in a manner that they don’t feel you are being a boss. There is a huge difference between being a boss and a leader and once you master that sorcery of being a leader, the chance of you becoming a manager increases.

4) Connect and Network With Other Leaders In The Company

This might sound political, but it is not. Connecting with higher management doesn’t make you a desperate person to get a promotion, rather it helps you understand the company’s processes better. A person from the higher management would definitely have more experience and knowledge than you and once you start conversing with them you start getting a clear view of how to pave your path towards becoming a manager that not only leads a team but also empowers and inspires.

5) Go Through Management Assessment

Almost every organisation follows the policy of Corporate management assessment. This is a process where the potential candidates are assessed thoroughly — whether it’s their leadership skills, mentoring skills or core knowledge of the domain. So, when you are provided with the opportunity to go through this assessment, make sure you take things seriously and perform well. Make sure you do your homework well on all the aspects that a data science manager need to cover.

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