SigmaWay Blog

Metrics are performance indicators for the markets. The rules in choosing the right metrics are:
1. Easy to use and understand
2. Easily replicated
3. Metrics should provide useful, actionable information that impacts the business.
With the availability of a wide variety of advanced analytics, it is easy to get sidetracked. Pressure to measure to many things makes it difficult o determine where to focus. Background data on customer is a useful metric. Effectiveness of targeting is related to marketers identifying customer personas. The right metrics such as calculating the potential lifetime values of various customers can help differentiate who is most likely to be profitable over the long term.
To know more: https://hbr.org/2015/07/identify-the-marketing-metrics-that-actually-matter

More often than not estimates, analytics, data-driven predictions seem confusing and overwhelming. But now the situation demands that benefits of data interpretation is vital. Statements such as data too difficult to access, understand or use are common and so are ignored while making business decisions. Fear of failure affects productivity and trying out new ideas. To dispel fear of data usage, managers need to promote better work ethic; data interpretation must start at the basic level with simple tools and incorporate the habit. To incorporate the total picture in a business decision, every perspective regarding data must be addressed.
To know more: https://hbr.org/2015/07/dispel-your-teams-fear-of-data

Today not only big companies are enjoying benefit of big data but also the small and medium sized companies are in the line. Many SME’s have become big data users. The size of data has categorized the type of data users. These are high data user, medium data user and small data user. While the high data users are more sophisticated in tools and more proficient in the storage and accessing of data, all of them share the same problem. Earlier SME’s were not able provide information to decision makers in a timely manner and they find difficulty in presenting and sharing information in proper formats. This problem led SME’s to access the big data technology which made it easier for them to present and share the information in easy to understand formats. Big data technology not only applies to private data but can also be applied to combination of private and public data. This is possible only due to the property of big data technology that it can handle both structured and semi structured data. Its design allows it to work on data stored on cloud and to deal with dynamic processing requirement. Big data technology is mature, flexible and affordable. It can alter the way of doing work with data, decision tools are easily accessible and information provided within the documents can have better view. When a company becomes large, its systems will become large, information and data become large leading to difficulties in handling them. Big data technology has provided a road to have more efficient systems and better techniques. Read more at:http://www.smartdatacollective.com/bruce-robbins/331325/how-business-users-will-benefit-joined-data

Like others, retailers also require advanced analytics to compete in the digitalized marketplace. With the expansion of Internet of Things (IoT), the effect of multichannel retailers will increase as they will start using advanced analytics. Advanced analytics, the analysis of data kinds using sophisticated quantitative methods that produce insights unlike the traditional approaches to business intelligence (BI).  These advanced analytics tools put information in the hands of business analysts and business users offering significant potential to create business value and competitive advantage. The need to improve real-time business decision-making will force retailers to acquire self-service and big data discovery capabilities. Read more at: http://www.analytics-magazine.org/special-articles/1352-retailers-need-advanced-analytics-to-compete-in-the-digitalized-marketplace

While you sit reading this article, if you stretch your arms, you would have electronic help all around you. A smartphone which could pay your bills, plan your schedule and tell you it’s time for a meeting or showing you the nearest food joints when you are hungry. These interruptions imply that we are surviving on advanced, analytics driven machine intelligence. All this said, for machine intelligence to be more powerful, we should be ready to accept a higher level of intrusion. For example, a patient detected with a heart disease, his device could suggest him to take a nap or hit the gym, or could wake him up when he is feeling anxious or stressed. Read more at: http://www.forbes.com/sites/teradata/2015/07/16/why-machine-learning-is-the-next-penicillin/

Flash vendors advocate the use of de-duplication and compression technologies combined with smaller space and lower power consumption. This means that when you buy 20 TB of raw flash capacity, the effective capacity is actually greater. But according to Michael Gunton, GM for Data Center Services, flash does not really reduce data footprints. Apparently data Scientists have seen trends of higher density infrastructure from the cloud providers. The benefits of consolidation are not apparent to ordinary business customers so they are not buying much flash to put in data centers. To know more, read: http://www.forbes.com/sites/justinwarren/2015/07/16/flash-not-reducing-data-center-footprints/

 After big data and internet of things, the new buzzword is the Analytics of things. Though on a similar note, we do not have an exact definition, we know what it means for the economy and the world, as a top strategic trend in technology. As better algorithms for IOT digital infrastructure are being built to index our world to every smaller level, connection based analytics can be used to better predict future conditions and prescribing future actions. AOT fuels the process as new devices are created, there is a potential for new analytics further leading to modification.  Read more at: http://www.forbes.com/sites/teradata/2015/07/15/analytics-of-things-what-does-it-mean-and-where-is-it-taking-u

Lack of planning and special interest pressure among politicians resulted in failure of building the necessary infrastructure required for adequate water retention which lead to water crisis in the state of California. Governor Brown is trying to control who can use water and for what it can be used efficiently. Charging people for water won’t be of any help but through the use of technology things like underground leaks, non-revenue water loss can be identified specially by using the ‘Night-time Flow Analysis’ solution given from Esri. This analysis works by using an optimal time to analyse for leaks mostly at night when consumption is low comparatively. It is a configuration of ArcGIS platform which includes sample data. The accuracy of leak detection depends on customer’s pipe network. The solution responds in real time and is a freely available configuration used to observe base flow conditions and control water crisis.

Read more at: http://www.smartdatacollective.com/shawn-gordon/325636/where-did-all-water-go

However developed and industrialized the country has become, still racism prevails in each and every corner of American society. And this is seen maximum in the workplace where someone named like an African American is less likely to get a call back from the company they applied for job. Also, women face a battle to succeed in the workplace which implied the gender pay gap. But HR analytics may help solve this where a data-driven department can better understand the statistics of recruitment decisions, retaining employee by evaluating employee turnover. But only if at first the attitude of individuals are changed towards others, then can analytics help ensuring that the same problem won’t return back. Read more at:  https://channels.theinnovationenterprise.com/articles/how-can-analytics-help-solve-diversity-issues

Sports analytics have become very popular now days. Predicting winners, player performance and team selection has taken a new form with the help of sports analytics. It has now become a new way of making money and building reputation in sports world. Analysts use the previous data to make predictive models and make future prediction using those models. There has been a shift from qualitative data that was traditionally used to quantitative data. Sports analytics have not been so easy in all sports. American Football, which has large number of variables that can change overtime, faces some difficulty seeking advantage of analytics. NFL teams hardly play 16 games a season implying very small sample size; it is very hard to get some pattern of data. Knowledge of the game and watching the games is equally as important as collecting data. In fact it is part of the data. Read more at:https://channels.theinnovationenterprise.com/articles/how-people-are-beating-the-bookmaker-with-sports-analytics

Data is the most crucial component of every organization but lately some businesses have started to deal in data. Therefore we need to focus on some basic steps before we begin to trade data and they are as follows:

1. Selling and Accumulating data - Companies that are engaged in selling data have suffered from serious adverse criticisms. If an organization has more data, then it has to bear higher risks and management costs.

2. Making Sense - Big data improves business efficiency and has assisted in the Internet of Things but management costs have to compensate against the money made.

3. Making Money - Organizations search for suitable techniques to create money from data.

4. Risks and Returns - Data helps to improve the quality despite of the risk aspects associated with it.

As the typical scenario in any data analysis includes more than one type of data source, working with large datasets, messy and unorganized data, there is a huge need of data prep required. Most data sets are relatively dirty and need to be thoroughly cleaned for the analytic result to be usable. The need to have some structure for reporting and analytical tools to grab onto resulted in a boom of data prep.

It is very imp to have the data validated in the initial stage, because if that goes wrong, then everything downstream of that becomes very problematic. Thus we need to have the data ready for analysis and to avoid any non-value add, which is achievable by big data prep.

Big data prep uses a combination of machine learning algorithm to automate most of the work that goes in sanitizing data. Read more here- http://www.datanami.com/2015/06/22/why-big-data-prep-is-booming/

Nowadays, business challenges across industries are same due to increasing commonality. Some of these challenges are customer satisfaction, market insight, cost reduction and supply chain efficiency. A solution to one of these problems in an industry can be applied to clients facing similar issue in another industry. This cross pollination can happen internally also between teams and departments. For prediction of the customer behavior we use analytical models by which we can give out the right message at the right time. These models can be used across industries to solve analytical problems because customers are often similar only and face same challenges. Read more here: http://www.callcentertimes.com/Articles/tabid/59/ctl/NewsArticle/mid/407/CategoryID/1/NewsID/1000/Default.aspx

External factors have a great influence on businesses thus understanding these factors is very crucial when building a statistical forecast. To know whether an external factor has influence on our analysis or not we should consider the following aspects-

1.Consistency- This means how volatile is the data.

2.Accessibility-This is the ease with which we get data.

3.Frequency of getting data- For yearly decision making a quarterly data would do good but for frequent decision making daily data fits best.

4.Data Granularity-The granularity of data refers to the size in which data fields are sub- divided.

It is an interesting conjecture that data can be thought to have a life cycle. This life cycle can be further broken down into phases. The first phase being data capture, creating data values for an enterprise. There are several ways for data capture. These are namely Data acquisition, data entry and signal reception. Data acquisition is the induction of existing data of an organization, data entry is the process of creating new data values for the enterprise and signal reception is the data generation process by devices working in sync with IOT. Data maintenance is the next phase of data life cycle. It necessarily deals with data synthesis and data usage. It includes processes such as movement, integration, data cleansing, extraction, etc. Read more at: http://www.dataversity.net/the-data-life-cycle-in-7-phases/

Understanding and collecting data is an important part of viable businesses nowadays. Big data helps us in that with its many applications in various spheres. Big data is not only limited to marketing applications, it can analyze structured and unstructured data searching for purchase patterns, build logs and store day to day information. Big data has helped optimize business performances, has led to an increase in productivity and thus it has made its impact felt on the profit margins. Big data empowers organizations with knowledge of their employees thus enabling interactions on an individual level. This is bound to make an impact on employee productivity eventually leading to growth of the organization in the long run. Read more at: http://www.business2community.com/big-data/big-data-a-big-impact-on-productivity-01274278

Big data and analytics are the buzz words in any industry now, but one should not forget that data inaccuracy can lead to huge losses for any industry. Big data becomes useless unless it possess a reasonable degree of accuracy. In case of industries like healthcare and banking big data mistakes can even take someone’s life. Data should be cleaned before data scientists can leverage it to derive useful insights. Practicing good data management is the need of the hour. Executives, instead of being impressed by the size of data, should question its quality. Systems should be designed in such a manner that it is able to simplify the process of data collection and minimize risks from inefficient data. Read more at:https://channels.theinnovationenterprise.com/articles/7782-big-data-vs-bad-data 

Other than having health implication, alcohol have a direct impact on match’s performance and cost the team valuable performance. So Arsène Wenger, manager of Arsenal started waving away team’s bad habits. His sophisticated approach and implemented policies, worked as catalysts for sport’s keen interest in analytics. The sports current value is predicted to rise due to the increasing accessibility to cloud computing, improved infrastructure allowing smart-phones and tablets play a bigger role in training programs.Arsenal’s rivals, Manchester United use analytics to maintain squad balance. Many sports are still unconvinced which shows that analytics still has room to grow. Other than elite sport, there is a rising valuation of sports analytics in amateur sport also. Major sporting institutions and amateur athletes still remain to tap in. Read more about it at:  https://channels.theinnovationenterprise.com/articles/sports-analytics-market-to-reach-4-7bn-by-2021

While running a business, one should consider not only internal data but external ones as well. Due to the inability to access information and integrate it, businesses are lagging behind. Though a huge amount of external data is available, it’s not always easy to find the desired information. Certain software aid the process of finding external data. Bringing both external and internal data together provides a unified view as well as helps in the process of decision-making and discovering insights. Data from all sources should be brought together and technologies that are able to perform this task and are able to put equal importance to all sources of data should only be accepted. Read more at:https://channels.theinnovationenterprise.com/articles/does-your-car-have-more-awareness-than-your-business

Nowadays, like other fields, big data insights affects strategies of digital marketers helping them to create effective campaigns. The discovery of various marketing technologies makes it clear that, companies are keen to invest in this. Sentiment Analysis tool is unethical but not illegal and is really helpful to find opinion –rich information to be acted upon. This “opinion mining” with the help of other tools can actually manage conversations about a brand when used with some other tools. But the drawback of it is we never know if the analysis becomes misleading and leads to the loss of a brand. There are many more such tools used. This proves the relationship between big data and digital marking has transformed into a more sophisticated one. Read more at:  https://channels.theinnovationenterprise.com/articles/where-big-data-marketing-meet