A major challenge facing data scientists or those in similar roles, is that business analytics is understood by few. Many of those who do understand are the younger lot. And many of these further only understand the mathematical concepts, but not the predictive aspects. This renders analytics as incomplete as the human element goes missing. The top Machine Learning tools such as Google’s Deep Mind or IBM’s Watson are powered by humongous streams of data warehousing. Yet, their iteration only when customer psychologies are well understood. A lot of stakeholders, even internal ones are skeptical on using analytics. Thus, the data models to be chosen are important. Cognitive biases and knee-jerk reactions imperil the proper usage. The presence of silos ensures that vital data cannot quickly travel across spaces.

Source:http://mitsloan.mit.edu/newsroom/articles/talking-to-your-boss-about-data/?utm_source=mitsloantwitter&utm_medium=social&utm_campaign=analyticstoaction

Uploaded Date:09 August 2018

Much is made of the gains through use if business analytics. Yet, the mere usage of a technology does not guarantee success without the raw material to process the same. Here, the right sources of data is essential. Once the data warehousing is done correct, the technology can be better applied to derive meaningful insights. The right data is now even easier to procure thanks to the reduction in rates for Internet of Things (IoT). An Insights Value Chain has been proposed by McKinsey which includes a combo of technical and business foundations. Technical involves data, analytics and the IT support, while the latter talks of people and processes. Data can be unstructured, so proper orchestration is required, including for newer sources. The privacy and legal considerations need to be checked along with its security. Analytics is about descriptive statistics, machine learning, optimization, simulation, cognitive modeling and classical predictive stats. The people part needs to take care of the right talent for analytics and a cultural change. Agile processes and a data governance loop make up the processes desired.

Source:https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/achieving-business-impact-with-data?cid=other-soc-twi-mip-mck-oth-1805&kui=PX8XZJaGLGPcax25MaoYIQ

Uploaded Date:25 July 2018

Almost every leadership team is imploring business consultants to explain how exactly can they take advantage of the huge hordes of data now available and the business analytics capabilities on hand. Many want to remain proactive against getting disrupted by unicorns such as Amazon or Netflix. Innovation centred around analytics is slowly catching up through the application of robotics, machine learning and automation tools. In a recent study undertaken by Bain, two-thirds of all surveyed confirmed that their companies have invested substantially in big data. Two-fifths of those want to see significantly positive impacts while a fifth of that number wants to experience genuine transformation through such investment. Certain recommendations have emerged form this study beginning with the focus on business science ahead of data science. The analytics adopted must be designed with a view towards service the last mile. Beyond the analytics in use now, companies must explore ways in which it can make an impact in the near future. Companies must be able to quickly execute idea that have been strategically planned. So once insights have been picked up, they must be quickly tested, from which team learn and further improvises. The transition between basic and advanced analytics is often as tricky as starting with it in the first place. This needs to be handled smoothly.

Source:http://www.bain.com/publications/articles/closing-the-results-gap-in-advanced-analytics-lessons-from-the-front-lines.aspx

Uploaded Date:24 July 2018

Swift technological prowess in fields such as robotics, data sciences, artificial intelligence and business analytics are fueling in new work opportunities, while reshaping the existing ones. These technologies themselves are not static and continue to evolve. This has massive implications for the larger economy.Opportunities are available now, but soon we will witness the next wave. Businesses are also looking at different approaches at data warehousing so that a lot of irrelevant information may be filtered out. Only that data is useful which will provide strategic insights. Deriving these insights is also an art, not just a scientific puzzle. Due to the dependence on data, legacy firms’ assets are getting underutilized. The extent of digitization varies unevenly across industries. McKinsey has even formulated a Digital Quotient Score to gauge the score lines for different companies. 84 was considered a high, and lowest was 4, with 34 out of 100 being bang average. Emerging economies are still well behind the developed ones in terms of leveraging its digital potential. Leaders amidst this situation must constantly experiment, learn and scale up fast. Existing business models need to be remodeled. The balance sheet now needs to incorporate intangible digital capabilities too. Humans and machines need to be aligned so they can work together.

Source:https://www.mckinsey.com/global-themes/digital-disruption/whats-now-and-next-in-analytics-ai-and-automation

Uploaded Date:23 June 2018

Companies have been experimenting with different ways to use and get benefitted from business analytics. As per an estimate by the McKinsey Global Institute, it can generate business global of between nine and fifteen trillion US dollars. Yet, many companies are not satisfied, claiming that this use of analytics is not giving them the right sort of business intelligence to base their decision-making on. So, McKinsey did a study spanning twelve different geographies for companies with total revenues in excess of a billion. Some leaders have indeed cracked the code of using analytics at scale. One of the key areas in which these leaders break away from the rest is that their entire strategy is aligned towards the analytics data. This data to be captured and further sieved through requires the right foundations in terms of both people and technologies available. Collaborative, cross-functional teams need to be put in place. The front-line staff needs to be empowered to make use of the data emanating. This helps analytics bridge the gap with the last-mile.

Source:https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/breaking-away-the-secrets-to-scaling-analytics?cid=other-soc-twi-mip-mck-oth-1805&kui=_0cl1m7qgzjLP6r7Apd54A

Uploaded Date:23 June 2018

Infrastructure developers are amongst the most asset-rich owners. They also generate enormous data during the project planning phase but are amongst the laggards when it comes to the use of business analytics. They are way behind industries such as retail, automotive and financial services. That is why their decision-making is too often qualitative rather than based on hard evidence. In order to embrace this technique towards a predictive system, infra companies must adopt a three-phase approach. It starts with design and data ingestion, followed by the proof of concept and then scaling. At the first phase, an exploratory analysis must be done. For this the data warehousing has to be done, as otherwise it will not be in the organized form the company would want. Only then will it be able to perform complex tasks such as correlation, natural-language processing, signal processing, outlier removal and data merge. At the next, algorithms need to be finalized and models validated. Now regression, clustering and network analysis will be possible. Permutation and holdout tests need to be performed. Before the final phase, an initial pilot has to be geared up. To close it proper scaling must be defined as the aims. The company must also ensure regular model maintenance.

Source:https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/how-advanced-analytics-can-benefit-infrastructure-capital-planning?cid=other-soc-twi-mip-mck-oth-1805&kui=EdiYuoYRMBBBALAPLIprKA

Uploaded Date:23 June 2018

Simply having truckloads of big data does not always equate to having the right kind of information. While a decade back, only the most forward-thinking and innovative companies were using data-backed business analytics, but now it is everywhere working on the who, what and where of customers. One of the earliest ventures still surviving is the Wharton Customer Analytics Initiative (WCAI) which recently completed its decade working. It started off with analyzing customer data in the music industry for Napster. The WCAI soon realized that the bigger slice of the bucks would come emanate from media and not entertainment, so the focus was adopted accordingly. A few years further on, digital advertising would get added on as the latest buzzword along with interactive media. The industry has evolved to have more internal teams, rather than any dependence on external agencies. Data has now become much more diverse so greater sophistication is needed to glean the right kind of information. There is now increased collaboration between marketing, research and the CFO’s teams. There has also emerged a subtle difference between the terms beginning with data- analyst, engineer and scientist. Going ahead, a greater amount of care will need to be taken by all agencies involved with data, due to the negative press arriving from recent violations of privacy norms.

Source:http://knowledge.wharton.upenn.edu/article/where-analytics-is-headed-next/

Uploaded Date:22 June 2018