SigmaWay Blog

SWOT Analysis must contain a two-dimensional look from both a product and a brand point of view. JIM JOSEPH (Contributor), writes in this article about some points which are important in any business. They are:

1. Business strength should become unique competitive advantage.
2. Challenge is the key word and focus on the factors that keep us from being competitive and identify our weakness.
3. Business house must think of various ways to expand their brand, because opportunities will be likely to impact business.
4. Business must identify the potential threats, and finally, focus on actionable items to make SWOT analysis realistic.

Read more at: http://www.entrepreneur.com/article/246434

In business, the 'black swan' is a form of disruption, which is usually least expected by a firm. Again, 'ugly duckling' refers to the day to day disruptions, which are always expected, and the firm remains prepared for it, hence, the coining of the term.  Companies don't pay attention to these disruptions with the expectation that they will remain small and never grow up to be as big and disruptive as the 'black swan'. Majority of the companies have risk management departments who focus on regulations and the traditional risks, completely ignoring the extreme ('black swan') cases. The 'black swan' has the capability of even surpassing the traditional data analytics procedures. Hence, comes into the picture, big data and advanced analytics, which gathers newer insights even about the most uncommon and worst case scenarios that may happen in the near future. Armed with information, much beforehand, the companies can make themselves immune, even to the worst of the situations. Predictive analytics thus plays a major role in recognizing 'black swan' events, often from the data of past 'black swan' episodes. Read more at:

http://www.teradatamagazine.com/v15n02/Features/Ugly-Duckling-or-Black-Swan/

The pursuit of data and analytics is helping companies to venture into new fields, transcending the fine lines between different industries. For example, a well-known sports company has ventured into the market of wearable technology, with the release of its fitness tracker, containing sensors, which automatically collects data regarding one's sleep patterns and the number of steps taken in a day.  Consumers must first download the fitness tracker app in their laptops or mobile devices. So, indirectly the company is also involved in the software business. Since the objective involves collection and analysis of data, to get customer feedback, the company is also involved in the analytics business. Cable and satellite suppliers are also using analytic tools to get deeper insights into viewing trends and ideas about more profitable programming opportunities. Read more at:

http://blogs.teradata.com/bill-franks/big-data-blurring-industry-lines-nike/

More and more music companies are turning to data analytics as people are increasingly listening to music online. Social media is providing newer ways to reveal the variety of factors affecting the different artists' sales. Music analytics companies now have access to information regarding the frequency, a song is heard, when it's heard and by whom, which help them in forecasting future hit records. Musicmetric, a music analytics platform, also focuses on gathering customer opinion from social networking sites to gain information regarding different artists' popularity for understanding the best platforms to market them. Even data related to ticket sales to live concerts can help in planning tours. To know more about music analytics, follow:

http://channels.theinnovationenterprise.com/articles/how-is-analytics-changing-the-music-industry

60% of CLOs are dissatisfied with their internal learning analytics capabilities. Most of the CLO are dissatisfied with their traditional organizational approach Kirkpatrick to evaluate learning. They want an analytical enhanced approach in learning. Modern analytical enhanced approach measure overall management and development of a company's employees. Laurie Bassi (CEO of McBassi & Company) and Dan McMurrer (Chief analyst of McBassi & company), talks about how to transform an organization's learning analytics:

1) Create an “authentic” learning impact evaluation by embedding it in a more holistic framework.
2) Stop waiting for the perfect data warehouse. Instead, create a “data hut.”
3) Don’t let the perfect become the enemy of the good.
4) Choose your initial analytics project carefully.
5) Start under the radar.
6) Insightful reporting trumps data dumps.
7) Use learning evaluation to improve the effectiveness of learning.
Read more at: https://www.td.org/Publications/Blogs/Learning-Executive-Blog/2015/05/7-Steps-to-Using-Analytics-to-Improve-the-Evaluation-of-Learning

Data analytics, especially the predictive ones, enable companies to obtain forward-looking insights they wouldn't otherwise have been privy to -- such as forthcoming changes in customer behavior and buying patterns, or the formation of new market segments as-of-yet unserved by the industry. The problem is that while you typically can't achieve the competitive edge you're looking for without analytics, you can't achieve it when you over-analyze, either which becomes a balancing act. Optimization is certainly a critical part of what companies should seek to derive from analytics, so too is innovation. Analytics becomes exclusively about finding ways to cut costs whereas also about finding ways to be cutting edge. It becomes about protecting today's existing revenue stream and should also be about finding tomorrow's new one. Thus, focusing too much on optimization can have the unintended effect of making organizations afraid to take on any risk. To know more, please read this interesting article by Joanna Schloss at: http://www.cmswire.com/cms/analytics/why-companies-cant-afford-to-go-overboard-with-analytics-029179.php

It has been documented that traffic can change for a certain website or app while reviewing analytics report. It is difficult to prepare an analysis as most often it is not clear from where to start the diagnosis. There is no best way to start the analytical reporting, the key being finding the exact reason behind the traffic drop; whether technical or marketing influence.  Traffic declines can be categorized into two category i.e. sharp declines or gradual decreases. Sharp declines over short stretches can point to technical issues while gradual declines may be inclined towards marketing and digital influences. Various solutions are available for change in traffic due to technical issues. To know more, follow:

http://www.cmswire.com/cms/analytics/where-did-it-go-understanding-traffic-drops-in-google-analytics-028468.php#null/  

Big data combines unstructured data with structured data to produce a report which provides a complete view of the organization. This report helps in decision making. Big data provide real time or near real time data. According to Mark Shacklett (President of Transworld Data), big data analytics results have impacted corporate revenues, expenditure and customer satisfaction. Web-based analytics on e-tail users helps in generating more sales by assessing customer preferences. Network diagnostics toolset analyze network and machine-generated data and produce predictive reports. This tool set gives a real-time view of network traffic. Sensors placed on railways and tram tracks, help crew members to proactively repair or replace equipment and railway tracks before they fail. Read more at: http://www.techrepublic.com/article/4-ways-real-time-analytics-lead-to-competitive-advantages/

According to James Heskett, Predictive analytics have the potential power to produce remarkable services and longer lives. Now, predictive analytics is also applied on humans. Recently, companies are sending information to target customers about products of their interest. With the advancements in big data and cloud computing, the accuracy of this science is improving now. Predictive analysis is also essential for organizations focusing on customer satisfaction in delivery time. Read More at:http://hbswk.hbs.edu/item/7527.html

We have come across the word "Smart city" frequently in recent times and we will see it more frequently in coming years. What exactly is a Smart City and how is it created?  The idea of smart City is to embed the technologically advanced devices which are making the Internet of things a reality into our surroundings. Once the technology is hard wired with the infrastructure, we can interact with these devices using our smart phones or computers and also devices can interact among themselves. For example, a car will be guided to an empty parking space without any human intervention.

In order to realize these smart cities, software and hardware applications for the internet of things are being encouraged. Some of the applications planned for smart cities are intelligent street lighting systems to conserve energy, mapping energy use around the city to understand demand and mapping how people maximize use of bicycle and foot paths. People are also raising caution over this new technology as it will hurt privacy.Read more at:http://www.forbes.com/sites/bernardmarr/2015/05/19/how-big-data-and-the-internet-of-things-create-smarter-cities/2/

In the age of Big Data, the data tables are going to grow enormously in size. Millions of rows of data will be obtained. Computational techniques available now will no longer be efficient in analyzing and interpreting the data. Enter this June; MIT researchers will present a new algorithm that will reduce the size of the data tables by leaving out bunch of rows. The data/rows remaining will be the representative ones of the total data. This is called condensed data/matrix of original data/matrix. To know more about how to handle Big Data, follow:https://newsoffice.mit.edu/2015/algorithm-shrinks-big-data-0520

Cloud Analytics is one application that is growing very fast and will accelerate in the coming decade. According to Ashutosh Kulkarni (Sr. Vice President and General Manager, Informatica Cloud), scalability, affordable price, simplicity to offer domain specific and user-specific capabilities make cloud analytics unbeatable. The scalability of Hadoop, and the emergence of highly elastic cloud analytic services like AWS, EMR and Redshift, Microsoft Azure, and Salesforce Wave Analytics, has helped in moving IT infrastructure to the cloud and cloud integration Platform-as-a-service (iPaaS) is designed to support the scalability, reliability and security of cloud analytics. Read more at: http://www.computerworld.com/article/2924846/data-analytics/the-future-of-cloud-analytics-is-now.html

Healthcare analytics, estimated to be over 20 billion, is growing very fast. The main reasons for the growth are: improved population health, reduced healthcare cost and patient safety. Thus, a vast majority of healthcare decision-makers have made analytics their priority. However, there are four main factors that could impact the adoption rate of analytics in healthcare. Data integration: It is the most important challenge because of the absence of unified datasets and inoperability between technologies.

Data breaches and security: This needs to be taken care off when data needs to be transferred to cloud in view of the recent healthcare data hacks.

Data sciences talent: There is likely to be stiff competition for talented data scientists with experience in healthcare and analytics.

Low current levels of analytics investment: The order of magnitude of investment in analytics may be the biggest challenge for the growth of analytics in healthcare.

Read more at:http://www.cio.com/article/2904270/healthcare/healthcare-analytics-4-things-impacting-the-adoption-rate.html

Instincts, intuition and creativity are invaluable when it comes to customers, but they cannot paint the whole picture. A solid grasp of data and analytics is required along with them to make a mark in the market. For example, logic does not come out as a winner when deciding loyalty but data and applied analytics tells us a different story. Results though counter intuition sometimes provide important insight which helps develop the marketing strategies. Advanced analytics ventures into the field of correlation and converts guesswork into evidence-based decisions. Advanced analytics also improves customer experience.  Sometimes it does not completely rely on intuitive results, deviates from it but helps to validate targeted campaigns.  To know more please follow the link:http://www.cmswire.com/cms/digital-marketing/they-love-me-they-love-me-not-data-analytics-can-tell-you-029247.php

Web-Connected devices that can talk to each other, transfer data, and carry out tasks without human interaction, are what make up the Internet of Things (IoT). These devices/ things are becoming popular among any firm looking for an edge. Generally IoT refers to devices in Consumer products such as Smart Phones, Smart cars etc. In Industrial sector these devices are used to build smart factories by integrating sensors and software to complex machinery. Growth of these devices will be very high as suggested by a report from Deloitte and there are only few firms working in IoT/Industrial Internet implying that there will be enormous opportunities for entrepreneurs in this sector.

As is the case with any budding technology, this area also faces challenges. Some of which are data security, addressing potential customers’ concerns about privacy, the lack of common standards between companies developing software for these devices; and the need to build physical infrastructure to support the collection and analysis of the data.Read more at: 

http://www.wamda.com/memakersge/2015/05/5-minute-guide-internet-of-things

•  Big Data enables various capabilities like forensics and the analysis of long-term historical trends. By collecting data and analyzing historical trends, you would be able identify when an attack started, and what were the steps that the attacker took to get a hold of your systems. These techniques could play a key role to detect threats at an early stage. Big Data provides  opportunity to consolidate and analyze logs automatically from multiple sources rather than in isolation. This enhances intrusion detection systems (IDS) and intrusion prevention systems (IPS). Integrating information from physical security systems, such as building access controls and even CCTV, could also enhance IDS and IPS to a point where insider attacks and social engineering are factored in to the detection process. This presents the possibility of significantly more advanced detection of fraud and criminal activities. Big Data could result in far more practical and successful SIEM, IDS and IPS implementations. Read more at-         http://www.techrepublic.com/blog/big-data-analytics/how-big-data-is-changing-the-security-analytics-landscape/

• Apache Hadoop- as software vendors are targeting the distributed storage and processing architecture, need for Hadoop is increasing 
• Apache Spark-The rapid rise of the in-memory stack is being extended as a faster and simpler alternative to MapReduce-style analytics.
• NoSQL-Databases like MongoDB and Couchbase are taking over jobs previously handled by monolithic SQL databases like Oracle and IBMDB2.
• Machine Learning and Data Mining- Big data pros who can harness machine learning technology to build and train predictive analytic apps are in high demand Statistical and Quantitative Analysis-Add in expertise with a statistical tool like R, SAS, Matlab, SPSS, or Stata is of high demand in today’s world. 
• SQL- SQL is still in demand for the next-generation of Hadoop-scale data warehouses. 
• Data Visualization-It has become most important in the job market. 
• Progamming Languages-Knowing programming languages like Java, C, Python, or Scala could give you the edge over other candidates. Read more at: 

http://www.datanami.com/2015/01/07/9-must-skills-land-top-big-data-jobs-2015/

Companies want to have an edge using analytics but struggle due to unavailable technologies, outdated software, cumbersome systems, complex integrations coupled with huge costs in infrastructure and personnel. The cloud has transformed the way organizations look at big data and analytics solutions. It has been predicted that investment in cloud over the next five years will grow threefold. • Open source platforms like Apache Spark provide simple and fast data processing capabilities. These though powerful, due to expensive hardware, long lead times are difficult to deploy. The cloud allows immediate usage of open source platforms without initial investments.
• Cloud -based software are user friendly, simple to grasp and sort in comparison to on premise counterparts and release cycles are shorter.
• Cloud-based analytics provide a platform where hard business problems can be effectively tackled.
• Processing and extracting data are easy from a synced centralized location.
• Better connectivity leads to better productivity, deployment of data pipeline is easy. Read more at:

http://insidebigdata.com/2015/05/08/5-reasons-data-analytics-in-the-cloud-will-take-center-stage-in-2015/

Natural disasters unlike other man-made disasters are the most terrifying events in the world since they cannot be controlled. However, by using the power of big data, it is possible to help in disaster management. For this purpose big data can be used through crowdsourcing which can be achieved by the increasing use of social- networking in the present days. In case of earthquakes, instead of using dedicated sensors which are highly expensive one can use the almost similar sensors in smartphones through Wi-Fi-hotspot and GPS to collect data to create an overall picture. For this, infrastructure needs to be set up so that information can be uploaded from the affected areas so that the affected people can be tracked down. Moreover the maps created through crowdsourced collaboration helps to optimize the recovery process. Read more at :http://channels.theinnovationenterprise.com/articles/big-data-in-a-crisis

Vendors often focus on showcasing their front-end capabilities, i.e. dashboard reporting and data visualizations, while ignoring the vital aspect of analytics, namely data preparation: cleansing, structuring and integrating data to make it ready for analysis. The typical scenarios include using more than one type of data source, working with large datasets, working with messy, unorganized data. This is where your business intelligence tools come in. These tools are meant to automate or simplify the bulk of the data preparation process by using pre-programmed adapters that connect into different types of data sources, and restructuring the data into a single centralized repository. Here are 3 crucial aspects of data preparation one should be aware of when evaluating business intelligence software: Access to the original data, joining multiple data sources and data management. Choosing the wrong software could skew your initial price estimate when you are forced to allocate technical resources or purchase additional programs to handle data preparation.

Read more at:

http://www.sisense.com/blog/data-preparation-checking-hood-analytics-software/