Tag Archives: Data Science

Data Science is the basic discipline which encapsulates data analytics, data mining, and big data concepts.

Top analytics trends 2017

Top analytics trends 2017 – An INFOGRAPHIC

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Here are the top analytics trends 2017 for businesses based on what industry and our clients are saying.

These trends reveal a pattern similar to the one observed last year. Embedded BI facilitates the analytics of everything on demand. Moreover, application of IoT devices continues to increase rapidly. Gartner estimates that 20.8 billion connected things will be in use worldwide by 2020.

While analytics, IoT and their applications in business continue to permeate deeper, artificial intelligence (AI) and machine learning (ML) is gaining further attention.

Artificial Intelligence and Machine learning is also the number 1 among the 2017 strategic technology trends report published by Gartner.

Until a few years back, mid-size organizations hardly considered AI as a possible solution to any of their problems. However, the pressure on margins due to increasing competitiveness fueled by online players is making it imperative for all businesses, big and small, to be more efficient.

Besides analytics, IOT, and AI, there is one interesting trend that silently continues to grow and intensify because of how human beings are evolving – the urgent need for clear, relevant, and crisp visualization of data.

According to a research by scientists, human attention span is shrinking so much that even a goldfish can hold a thought for longer. The study by Microsoft says that average human attention span has fallen from 12 seconds in 2000, (or around the time the mobile revolution began), to 8.25 seconds in 2015.

While the comparison with the attention span of goldfish is debatable, the underlying insight – that humans are less attentive than ever before – hardly is. Powerful visualization of information remains the key.

Another trend catching the attention of businesses is the use of predictive analytics. In today’s uncertain business environment, companies want the ability to forecast future business performance based on the past. Predictive analytics tries to answer questions such as: What is likely to happen tomorrow? How can we make the business improve? Consequently, predictive and prescriptive analytics are among the most discussed analytics trends among the professionals.

In summary, smart businesses are recognizing the contribution of analytics (and the associated technologies) in their ongoing success. The top analytics trends 2017 continue to reflect this new reality. Unfortunately, Business analytics talent is scarce. Companies are struggling to hire (and afford) the right people that will help them realize the true benefits of analytics. This makes it ever-more critical to engage with partners that will bring on-board the right combination of computing know-how, analytical and visualization skills, and business acumen.

So, here are the top analytics trends 2017 at a glance. Do read-on, review and respond.

Download PDF of Analytics top trends 2017

 

Top analytics trends 2017

Top analytics trends 2017

 

 

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Top Analytics Trends 2016 for SMBs

Top Analytics Trends 2016 for SMBs

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Like last year, we bring to you top analytics trends 2016 for SMBs based on what industry stalwarts and our clients are saying.

A quick comparison with last year trends reveals that some trends continue to evolve. Topics like Deep learning, Self-service-BI, or Cognitive computing are some latest ones being discussed. Nonetheless, others are rapidly gravitating towards some common theme. One such theme is ‘Big Data Analytics’. More and more small and medium-sized businesses (SMBs in short) are going digital, embracing analytics, and leveraging their data to turn insights into higher revenues, reduced costs, and overall business growth. According to analysts, the big data analytics market is expected to reach nearly $50B by 2019.

Our Trends focus on applicability of these technologies to small and medium business (SMB) organizations. Although, technology indeed plays a vital role in running a successful business, some of these emerging technologies are not immediately relevant to SMBs. It can be helpful to develop an awareness of these technologies but very few SMBs are actually going to use say, 3D printing or AI in 2016 – and nor should they if they have no real business benefits.

This year, we have identified nine top analytics trends that are most relevant to SMBs.

 

1. More SMBs use analytics for business benefits.

Until a few years back, big data (analytics) was more hype than a reality. Google was awash with searches involving keywords centered on ‘Big Data’. However, over the last couple of years, analytics has left the hype-curve to provide real value. Today, (big data) analytics is everywhere.

Earlier, SMBs were not too savvy about maintaining the data of their customers, product-orders, and suppliers. This was largely because of the costs involved in data storage without the apparent benefit of maintaining the data. However with the data explosion through various media over the last couple of years and with the availability of custom-analytics providers, they woke to the possibility of utilizing their data for getting answers to some key questions around their businesses. As the benefits started becoming visible – in terms of exponential business growth in a few cases – SMBs started focusing on analytics and become more data-driven to improve their business results.

However, making this data meaningful and easy to understand is still a challenge for many. We think that 2016 will be the year that small-scale analytics will really take off for SMBs, as it allows them to leverage their data from disparate data sources for their business benefits.

 

2. Internet of Things (IoT) enters our daily lives.

This is what Nikola Tesla said in a 1926 interview with Colliers magazine:

Top Analytics Trends 2016 - Internet of Things globe pic

IoT world

When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole… and the instruments through which we shall be able to do this will be amazingly simple compared with our present telephone. A man will be able to carry one in his vest pocket.

How it has become a reality less than a century later!

 

The Internet of Things (IoT) in its current form proliferated with the surge in low cost sensors embedded with Bluetooth wireless capability onto a small chip. And it is rapidly evolving from the realm of fascinating gizmos to real-world utility gadgets. Many leading companies such as Google, Amazon, Cisco, Dell, and TI have developed their versions of IoT products. There are already some cool IoT devices like Nest, Fitbit, and Belkin, to name just a few, that are vying for consumers’ attention in the market.

So what’s in it for SMBs?

In terms of Google trends shown in Exhibit-A, IoT today is where big data analytics was around 4-years back. Gartner forecasts that there will be 6.4 billion internet-connected things in 2016. Although the potential of IoT is huge, few SMBs consider it their ‘critical’ priority for investment at the moment. For them, it is still a nice buzzword. There is still time before every visible thing will have a sensor attached to it that will communicate with your servers in real-time. Meanwhile, SMBs are willing to watch and ride the hype-cycle.

Top Analytics Trends 2016 - IoT interest over time (Google graph)

 

3. Predictive analytics to address cybersecurity concerns.

As SMBs expand their technology footprint to run their business operations, the need to secure and protect data grows. Data security and privacy concerns continue to exist among small and large business organizations. However, many SMBs feel challenged and intimidated to deal with the rising complexity of cybersecurity breaches. Companies are normally content with the conventional approach of putting defensive mechanisms to ward off security risks. However, with technology advancements, the security breaches have also become more sophisticated and more risky wherever consumer data is involved.

While large organizations invest heavily into advanced (read: expensive) security mechanisms, SMBs do not have the luxury to do so. Nevertheless, they are now custom-developing predictive analytic models to proactively monitor log files and other user data sources to detect any threat perception or breach alerts. Clustering algorithms can help them identify anomalies in user login or other events which can be recorded on an ongoing basis. 2016 is likely to see an increase in the application of predictive analytics to deal with cybersecurity concerns.

 

4. Machine Learning algorithms foster man-machine collaboration.

We are entering the ‘smart’ era – smart people working alongside smart machines in smart cities. IDC¹ predicts that companies will spend more than $60 billion on cognitive solutions by 2025. Theoretically, machine learning algorithms based on neural network and AI have existed for a long time. However, their widespread application in everyday life is getting acceptance only now. This is made possible due to the tremendous increase in processing power that enables real-time split-second decision making.

Machine learning algorithms are currently being employed primarily in retail industry. With more people shopping across multiple channels looking for lowest prices, machine learning algorithms will become very popular in implementing dynamic pricing and devising on-the-spot offers in retail stores to retain the buyer.

For example, during this year’s holiday shopping season, leading retailers such as Amazon and Walmart were relying heavily on algorithmic pricing. Both retailers re-priced 15% of 18,000 product SKUs being tracked by a pricing intelligence solution on November 14th alone. These algorithms will be the backbone of any and every e-commerce business striving to win and retain customers.

[Example credit: Forbes]

 

5. Rising smartphone and tablet penetration continues to increase consumer mobility.

According to a comScore – Morgan Stanley research, mobile users globally have surpassed desktop users at the beginning of 2014. Rising mobile adoption, among people of all ages, impacts consumer purchasing patterns in a big way. With the increasing mobility, SMBs view mobile apps as a way to reach and engage end-users. SMB Group’s 2014 SMB Mobile Solutions Study indicates 59% of SMBs view mobile solutions and services as ‘critical’ to their business.

 

6. Hybrid cloud options still complex for SMBs.

2015 saw cloud making deep inroads into data-centers, data warehouses, centralized storages, and servers. SMB group’s market study shows that the cloud is poised to overtake on-premises deployment in the next year in areas such as collaboration, file sharing and marketing automation.

However, SMBs are largely using public cloud and staying away from private (or hybrid) cloud options because of the lack of clarity. Microsoft, Dell, and IBM have their own cloud platforms as hybrid cloud options however they do not yet seem to provide a compelling proposition for SMBs to embrace.

 

7. Omni-Channel integration or cross-device challenge?

Omni-channel is not a buzzword anymore given the availability of multiple screens every customer has. People have indicated that they love to shop across channels. So, more and more brands are going omni-channel way in a bid to woo consumers and to help them buy in their preferred channels. Brands are applying strategies like location based analytics to make relevant offers when consumers are in the vicinity of their stores. Businesses (like Macy’s or Virgin) that offer a unified omni-channel experience to their customers appear to have a competitive edge over others that cannot.

However, in a March 2015 study by Signal, 51% of marketers worldwide reported that they did not have a single view of customers, and only 6% of marketers worldwide reported they had an adequate single view of customers or prospects across all devices and touchpoints.

From our perspective, this year businesses will make this decision of whether they will play the omni-channel game and how.

 

8. Real analytics talent is (still) scarce.

According to the 2015 MIT Sloan Management Review survey² of business executives, managers and analytics professionals, 49% of respondents, who believe analytics creates competitive advantage for their organization, say that their company lacks appropriate analytics talent. While there is no dearth of analytics CVs in the market, very few of those appear to have real data science skills. In reality, companies need data scientists who possess the rounded knowledge of computer science, algorithms, math-statistics, business, and analytical skills.

Organizations that hire the less than appropriately skilled analysts end up wasting more than just money without any real benefits accrued. This is the reason more than 50% of analytically challenges organizations have stated that they outsource analytical services to external consultants or organizations, according to the survey.

Smart companies are realizing that analytical talent is critical to their success and in short supply, but more than 40 percent struggle with finding the talent they need. There seems to be a growing belief among SMBs that it is best to focus on customers to grow business leaving the necessary analysis tech work to specialists.

 

9. Visualization will be vital to SMBs application of analytics.

Data and the insights from that data are no more relegated only to the analysts. Business owners and SMB executives want to visualize their data to understand ‘what’s really going on’ in their business. And they want to do it in minimum possible time to be able to focus on the more important aspect of applying those insights for the improvement of their business. Business analytics in the SMB space is likely to stop being just a set of bar- and pie-based charts, and will be more multi-variate and intuitive. SMBs will demand more from the analysts in terms of visualization techniques that makes it easier and faster to visualize, understand, and explore data and uncover real insights from it.

 

Conclusion

This is an extremely exciting time for SMBs who can now apply customized analytics as per their specific requirements to take their business to a new level in an economical way. We believe this was not an option they previously had. It will be interesting to see how SMBs embrace business analytics to leverage the opportunity and explore unlimited possibilities.

 

References:

1 International Data Corporation

2 Ransbotham, D. Kiron and P.K. Prentice, “The Talent Dividend: Analytics talent is driving competitive advantage at data-oriented companies,”MIT Sloan Management Review, April 2015.

3 IoT image credit: wikipedia

 

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The most important thing in analytics - DART hitting dartboard at the centre

The Most Important Thing in Analytics is… No, It’s Not Data!

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L  ast Friday, I was having a very productive give-and-take with a group of business people on the benefits of business analytics. During our conversation, I had an interesting observation about what those people considered the most important thing in analytics. They seemed to think that data is the most important element in any (business) analytics project. I am sharing here the gist of that conversation. Hope you too find it interesting.

I believe data is the second most important thing in analytics or data science.

The most important thing in analytics is ‘the question to be answered’.

Let me explain why.

Typically analytics initiatives are undertaken to achieve one of these four objectives:
1. To solve a problem
2. To accomplish a specific (business or non-business) goal
3. To prove or disprove a hypothesis
4. To answer a question

Unless you have a specific question that needs answered, any analytics initiative will at best be a random analytical study with no direction. The specific question or the problem statement gives it a definite goal. More importantly, the goal will determine what data to use to arrive at a fact-based conclusion.

I like to think of it as ‘a ship sailing in an ocean’ analogy. An analytics initiative making way through the huge organizational data is analogous to a ship sailing in an ocean. If the ship has a pre-determined destination, then it takes the intended route to reach that destination and achieves its objective. To a bystander, mere existence of a ship creates a perception that it is going somewhere. However, if the destination is not clearly defined, the ship might just have a fun time cruising the ocean and figuring out what the ocean has to show. Worse, it might just flounder in the huge ocean and may not reach any meaningful place.

A ship like that is like a holiday-cruise-liner – expensive to sail but not tasked with any objective to reach a place.

Likewise, many organizations launch their analytics initiatives without any specific target. They just start with a tentative goal of let’s figure out what the data shows along the way. In the absence of any pointed objective to pursue, the analysts running the initiative either have great fun cruising with unbounded data or they just lose their way in it. Such an initiative renders no meaningful results.

An analytics initiative like that ends up becoming an expensive proposition like a luxury cruise-liner.

That’s why the most important thing in analytics is the question to be answered. The question determines what data to use to reach a definitive answer.
For example, to determine an organization’s target customer profile and their buying habits, the customer data will need to be analysed. Similarly, the sales history data will be required to ascertain a company’s sales trends.

The data serves as the route to finding the answer to the question.

The data to be analysed will change with the question to be answered. In our analogy, it is the route taken by the ship to its destination. Different destinations will warrant different routes to be pursued.

The selection of the right data becomes important after determining the objective(s) of the analytics initiative. The right prioritization will not only help you reach your destination faster, but it will also be very cost-effective as you save yourself from expending time, money, and manpower in analysing needless data.

In a nutshell, make sure that you do not end up getting lost in the ocean of data with wrong prioritization. Data-cruising is an expensive way to have fun at the expense of an organization’s usually scarce resources.


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