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The start of modern data science can be traced back to Google search ranking optimization and for LinkedIn recommendations. But now data science is used across all fields. Yet, the terms data science and data scientist aren’t that well understood. One of the key tasks for data scientists is to build a solid foundation on which business analytics may be performed. Machine learning pipelines and personalized data-backed products then need to be worked on. Contrary to the usual perception, data science is now being performed in several industries beyond tech, such as healthcare, travel bookings, restaurants, ride-sharing and much more. More than tall promises such as artificial intelligence or autonomous cars, it is about facilitating the daily processes. The skills needed by data scientists is also constantly evolving. At present for instance, a disproportionate amount of time is getting spent on cleaning and optimizing the data. Specialization is getting increasingly important. Data science is now being worked on in three broad areas which are- business intelligence, decision science and machine learning. A major challenge right now and only expected to expand is the ethics in the field. Privacy and data protection are two of the crucial aspects where ethics could get challenged.

Source:https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists

Uploaded Date:27 November 2018

Seven ways have been identified for making best use in marketing of business analytics. The first such method is through chatbots. The next one is by providing personalized content. This is now possible because analytics tools can process granular bits of information to understand the key characteristics of individual users. This will also specify the digital marketing campaign as specific ads can be cropped up for particular audiences. The same logic will apply on social media as well. Another related area of massive help to sales professionals is lead scoring. It helps identify which leads and the sources which bring maximum value. Personal Identifier Information (PII) can help with detecting fraud and improving cybersecurity. Speech analytics can be a part of this to track call transcripts. Once the right keywords have been identified, marketers can always integrate the top twenty words into the CRM. This helps alignment between the sales process and the CRM. This is of much value to call centre managers in particular.

Source:http://www.bandt.com.au/opinion/seven-ways-apply-speech-analytics-marketing

Uploaded Date:23 November 2018

The way the business world is getting impacted, in the coming years, it is the intelligent enterprises alone that will survive and thrive. These are firms that can anticipate the evolving challenges, converting them into opportunities. The global revenues for big data and analytics solutions is expected to reach US$ 260 billion by 2022, which represents a CAGR of 11.9 %. For companies, extracting key business insights becomes crucial. And now more than ever before, data warehousing is essential. This data is then processed using predictive analytics, so firms can proactively approach the opportunities knocking on the door. The right storage of data is necessary so information may be leveraged out of it.

Source:https://www.analyticsinsight.net/analytics-transforming-industries-with-unprecedented-business-insights/

Uploaded Date:21 November 2018

By now most companies have realized the importance of Advanced Analytics (AA) to their marketing strategy and decision-making. Unfortunately, as it emerged from a study conducted by McKinsey, less than a fifth of those polled have truly leveraged the vast scope of business analytics. Most firms start eagerly, but without any clear-cut strategy. As a result, they initiate several pilot projects but fail to scale up. While companies are engaged in storing huge quantities of data, due to lacking any end-to-end approach, they fail to leverage the same. Thus, before pursuing any such AA-modeled transformation, companies ought to ask themselves some clear question beginning with the structure of the organization. This involves whether it must have a centralized, decentralized or even a hybrid model. Firm decisions must also be made on whether any part of the work is to be outsourced or not. Finally, one has to decide where to locate the AA unit. Due diligence must be put in while staffing the members at the AA centre of excellence. The right corporate training programmes need to be initiated for the concerned staff members. And likewise, the right strategic partnerships signed. This training must focus on matters beyond the quantifiable, but on the cultural transformation to be effected.

Source:https://www.mckinsey.com/industries/financial-services/our-insights/building-an-effective-analytics-organization

Uploaded Date:14 November 2018

In common parlance the fashion and apparel industry seem to have little to do with advanced business analytics. Apparel makers and retailers have traditionally been stronger on the art side of business, taking decisions based on judgement and intuition rather than on the science of such quantifiable analytics. That would need to change, as demonstrated by Amazon’s use of the same. Some actions have thus been identified which all apparel retailers need to apply in order to improve their business. Firstly, they must know how to prioritize when it comes to markets and segments as not all have equal potential. Data warehousing is to play a massive role, as this vast treasure trove may get analyzed to extract meaningful business insights. Instead of continuously keeping an eye on the calendar, due focus must be made on the value addition. The recruitment has to be well thought out with the right mix of designers representing the art and the data scientists representing the science side.

Source:https://www.mckinsey.com/industries/retail/our-insights/geek-meets-chic-four-actions-to-jump-start-advanced-analytics-in-apparel

Uploaded Date:13 November 2018

Major marketing decisions are no longer made on the basis of intuition or experience. Thorough use is made of big data. These data sets are gargantuan enough that traditional data processing systems are unable to cope with them. Google Analytics is the perfect tool to gauge the kind of insights such data sets may provide. It also helps in making digital marketing campaigns more successful by first of all designing better campaigns. The market intelligence received also helps in setting more pertinent pricing. This is especially relevant for the B2B segment due to the singularity of each occasion. This intelligence gathered on consumer needs, helps funnel the right web content as well. Netflix is an example of a company that uses this data to disburse personalized content suited to different market segments.

Source:https://www.forbes.com/sites/theyec/2018/11/08/how-can-big-data-contribute-to-digital-marketing-success/#253aeaa1ca80

Uploaded Date:12 November 2018

Machine Learning (ML) iteration is not the simplest tasks due to the fact that the requirements constantly move goalposts. Plus, due to a lack of standardization, there are no adopted benchmarks against which to deploy one’s latest development. Due to this reason, developers must make ample use of the existing tools for its development. Some such common tools include Scikit-Learn, Pandas and Feature-tools. There are three steps that need to be followed in the entire development cycle. To start with is Prediction Engineering. This is about setting up the ML problem. Next up is Feature Engineering which iterates how ML gets powered. And finally, there is the Modeling phase. This is the business end of things where one works with algorithms. This algorithm in turn uses the business analytics model to make predictions from the existing data of possibilities.

Source:https://towardsdatascience.com/how-to-create-value-with-machine-learning-eb09585b332e

Uploaded Date:10 November 2018

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