Learnings – Alignassociate

Data lakes: the what, why and how of data lakes.

HIGHLIGHTS:

A data lake is a place to put all the data enterprises (may) want to gather, store, analyze and turn into insights and action, including structured, semi-structured and unstructured data
Data lakes are storage repositories with an analytics and action purpose
Data lakes are designed for big data analytics and to solve the data silo challenge in big data

HIGHLIGHTS:

Upgrading the World: Predictive analytics serves that very purpose by driving mass-scale processes empirically, guiding them with predictions generated from data. Millions of predictions a day improve decisions as to whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date and medicate.
Reinventing Industries:

Marketing: predictive remarketing

Financial services: Paychex, Chase, insurance study

Workforce management: Walmart, Wells Fargo, via Facebook data

As predictive analytics’ adoption widens and deepens across sectors and across organizational functions, an inter-industry synergy emerges. Stories are shared between sectors, and the lessons learned and proof-of-concepts viewed from neighboring industries inspire and catalyze growth, creating a cyclic effect.

HIGHLIGHTS:

57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support.
Analyze and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models.
By 2020, real-time personalized advertising across digital platforms and optimized message targeting accuracy, context and precision will accelerate.
Price optimization and price elasticity are growing beyond industries with limited inventories including airlines and hotels, proliferating into manufacturing and services
Improving demand forecasting, assortment efficiency and pricing in retail marketing have the potential to deliver a 2% improvement in Earnings Before Interest & Taxes (EBIT), 20% stock reduction and 2 million fewer product returns a year

HIGHLIGHTS:

Analytics Adoption Rises Among Small Companies: In the SAP survey, 73% and 87% of small and midsize businesses surveyed indicated that their expectations regarding technology investments were met or exceeded. With the cost of data analysis and visualization technologies falling and investments bearing fruit, this year should see the adoption of data analytics extend to even the smallest of companies.
Outsourcing of Analytics Increases: One option for SMEs unable to fund full-scale programs is to outsource them to an outside agency that specializes in data analytics. Outsourcing analytics is an excellent way of enhancing your data capabilities when you lack the necessary funds, making it ideal for small companies
Qualitative Data Is on The Up: Qualitative data bridges knowledge gaps. It is the information found in the unstructured data of online reviews, social media, and so forth, that provides the context to help understand why something is the way it is and if it is changing.

HIGHLIGHTS:

Welcome to the AI gold rush! : AI and machine learning will become ubiquitous and woven into the fabric of society. Media headlines tout the stories of how AI is helping doctors diagnose diseases, banks better assess customer loan risks, farmers predict crop yields, marketers target and retain customers, and manufacturers improve quality control.
So where is the value being created with AI? Who will make money across the (1) chip makers, (2) platform and infrastructure providers, (3) enabling models and algorithm providers, (4) enterprise solution providers, (5) industry vertical solution providers, (6) corporate users of AI and (7) nations?
Who’s got the best AI chips and hardware? Even though the price of computational power has fallen exponentially, demand is rising even faster.
Who’s got the best infrastructure and platform clouds for AI? oday AI is demanding so much compute power that companies are increasingly turning to the cloud to rent hardware through Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) offerings.

HIGHLIGHTS:

Most analytical jobs such as that of a Business Analyst (BA) require critical thinking and rethinking decisions based on dynamically changing situations. Can Artificial Intelligence and Machine Learning systems branch off to different thinking modes as humans do based on changing parameters?
Is the Threat of AI Technology Real? : With the re-emergence of Machine Learning (ML) research over the past decade, the global IT giants all rushed to take the lead role in bringing automated systems to the white-collar professions. In the last five years, we have witnessed a meteoric growth of robots, digital assistants, smart machines, automated bots, and apps in every industry sector from manufacturing to marketing.
Use of AI in Different fields
The Future Evolution of the Business Analyst: If humans and machines learn to work together now, then very soon the “physical-digital teamwork” can transform workplaces around the world.
The Hybrid Roles for Future Business Analysts: The Hybrid BA can well serve the technical team by wearing the domain-expert hat or the BA hat as needed.

How Big Data Science and Analytics is the Lure for Businesses Today

HIGHLIGHTS:

Coexistence of Two Systems: The leveraging of machine learning and traditional algorithms to analyze the Big data for any organization can solve problems in multiple verticals and forecast the business future with greater speed and reliability.
Point Solutions: Data analytics has been in the Business Intelligence space for quite a long time providing ‘Point solutions’ for specific problems in any business.
Advantages of Using Big Data Analytics:
Turnaround speed: virtually adopting any data source, and ability to churn much greater volumes of data.
Hybrid data clouds: Hybrid data clouds are being implemented, that divide data and work zones between in-house and offshore. As a result of these two complexities, the Decision makers of the companies, IT heads and service providers have to actively design the Big Data Ingestion Pathway, otherwise it could diminish the ROI significantly.
Impact of Big Data:
There is tremendous scope for machine level or customer interface level interventions to increase the business opportunities manifold. These interventions typically include customer marketing opportunities and risk reduction needs.

HIGHLIGHTS:

Dark Data: data that has been collected, but is unstructured and, therefore, not currently being used.
Real time Example of Dark Data: One example of dark data is a customer call record. Potentially holding valuable information on a customer’s thoughts and geolocation, these types of records are regularly recorded and stored, but rarely organized or analyze

To read more:https://medium.com/@jillplatts/what-is-dark-data-1beef317bb2e