10 Misconceptions about Big Data

10 Misconceptions about Big Data
10 Misconceptions about Big Data

Lot of content is thrown around Big Data, and how it could change the market landscape. It is running it’s hype cycle and many bloggers, experts, consultants, & professionals are lining themselves to align with big data. So, not everything that is known to this industry is accurate and some misconceptions that show up here and there have been discussed below.

  1. Big Data has the answer to everything: This has been floating as part of many coffee table conversations. Big data could help but it’s not a magic wand that will help you find answers to everything. Certainly Bigdata could potentially help you answer most cryptic questions but it’s not for everything. So, fine-tune the expectation of what to get out from BigData strategy.
  2. Data Scientist drives BigData: Almost every now and then, we stumble upon someone who claims to be a data scientist and boast about how they are driving BigData in their company. Surely, they all are doing important work of helping find insights from data but BigData is already happening whether Data Scientists drive it or not. BigData journey begins with capturing as much data as possible. Data scientists just help steer the insights from data. So, don’t wait on data scientist before you start prepping for BigData.
  3. Big Data is complicated: With escalating pay cheque of Data Scientists, it is not difficult to understand that BigData is perceived as rocket science that only few could tame and understand. This is a pure evil that most of businesses has to deal with. BigData is just more volume, velocity and variety of contained data. It need not be complicated. In fact, a well-designed big-data system is often scalable, simple and fast. So, breath easy if you find your big data nicely laid out and easy to understand.
  4. The More Data the better: There is a debate on how much data is effective and whether more data is better. There are certainly two schools of thoughts. One suggesting that more data you have better you could learn from it. But I believe data and its effectiveness goes more around quality aspect of data and not always quantity. So, based on the circumstances, quality and in some case quantity could signify better impact from data.
  5. Big data is just hype: Surely, you must find yourself either for or against this statement, but that is because it is what it is. Bigdata is getting a lot of press hours and PR time. It is partly because there is hype, but partly because tools to deal with big data has unveiled the capability to address unmanageable blob of data and parse to insights using commodity hardware. So, hype is evident but there is whole capability shift that is fueling this hype like demand to handle more data and get to better insights within data. So, big data is not just hype but a real shift of capabilities on how businesses start to look at their data.
  6. Big data is unstructured: Yay, I am certain if you are into bigdata domain for more than 1day, you must have heard rants around bigdata being unstructured data. It is not true. As stated earlier, big data is just data that is beyond your expectations around 3 vectors: Volume, Velocity and Variety. So, data could be structured or unstructured, its 3 Vs that defines its BigData status and not the structure of data.
  7. Data eliminates uncertainty: Data surely helps convey more information around a particular use case but certainly it is not an indicator to predict certainty. Future data is as uncertain as the market condition. Uncertainty comes to business layers through various areas, competitive landscape, customer experience, market conditions, and other business dependent conditions. So, data is certainly not a good indicator for eliminating uncertainty.
  8. We must capture everything in order to analyze our Big Data: Sure, it sounds awesome to capture everything to learn everything, but it’s delusional. Everything is a very circumstantial thing. Business every time shifts its dependence from few sets of KPIs to others. So, there could never be an exhaustive list to capture. It will keep on changing with market. Another key area to understand is that few data sets have limited to no impact on business, so data should be picked according to it’s impact on the business. And these KPIs must be evaluated every now and then to measure changing market shift.
  9. Big Data systems are expensive to implement and maintain: Yes, this still exists as a misconception with many businesses. But businesses should try to understand that the very fact Bigdata is sitting in hot seat is because commodity hardware could now be used to tackle bigdata. So, bigdata systems are not expansive anymore. They have been low and getting lower on their cost. So, cost should never be a deterrent for indulging in bigdata project.
  10. Big Data is for Big Companies Only: Like the point previously quoted, big data tools are cheap and they are run on cheap commodity hardware. So, they are accessible and no more the dream/passion of big corporation only. Small/Mid size companies have almost similar leverage when it comes to thinking like big corporations. So, bigdata capabilities are for the strong hearts and not the rich pockets.

So, BigData landscape is filled with Truth and Myth, so make sure to check which side your hurdle lies before calling in quits and throwing in the towel.

Source: 10 Misconceptions about Big Data

Gleanster – Actionable Insights at a Glance

Gleanster: Actionable insights at a glanceI am happy to announce that I have joined Gleanster’s Thought Leader group as a contributing analyst. Gleanster is a market research and advisory services firm that benchmarks best practices in technology-enabled business initiatives, delivering actionable insights that allow companies to make smart business decisions and match their needs with vendor solutions.

In my role at Gleanster, I will be involved in providing insight into the Enterprise Feedback Management (EFM) and Customer Experience Management (CEM) space. Building on Gleanster’s 2010 Customer Feedback Management report as well as my own research on best practices in customer feedback programs (See Beyond the Ultimate Question for complete results of my research), I will be directing Gleanster’s upcoming benchmark study on Customer Experience Management. In this study, we will identify specific components of CEM that are essential in helping companies deliver a great customer experience that increases customer loyalty.

“We are excited to have Dr. Hayes as part of our distinguished thought leader group. Dr. Hayes brings over 20 years of experience to bear on important issues in customer experience management and enterprise feedback management. Specifically, his prior research on the measurement and meaning of customer loyalty and best practices in customer feedback programs has helped advance the field tremendously. His scientific research is highly regarded by his industry peers, and we are confident that Dr. Hayes’ continuing contributions to the field will bring great value to the Gleanster community.”

Jeff Zabin, CEO

As a proud member of the 1% for the Planet alliance, Gleanster is committed to donating at least 1% of their annual sales revenue to nonprofit organizations focused on environmental sustainability.


How oil and gas firms are failing to grasp the necessity of Big Data analytics

An explosion in information volumes and processing power is transforming the energy sector. Even the major players are dragging their feet to catch up.

The business of oil and gas profit-making takes place increasingly in the realm of bits and bytes. The information explosion is everywhere, be it in the geosciences, engineering and management or even on the financial and regulatory sides. The days of easy oil are running out; unconventional plays are becoming the norm. For producers that means operations are getting trickier, more expensive and data-intensive.

“Companies are spending a lot of money on IT. Suncor alone spends about $500 million per year.”

Thirty years ago geoscientists could get their work done by scribbling on paper; today they are watching well data flow, in real time and by the petabyte, across their screens. Despite what many think, the challenge for them doesn’t lie in storing the mountains of data. That’s the easy part. The challenge is more about building robust IT infrastructures that ­holistically integrate operations data and enable ­different systems and sensors to talk to each other. With greater transparency over the data, operators can better analyze it and draw actionable insights that bring real competitive value.

Even the big guys aren’t progressive in this area,” says Nicole Jardin, CEO of Emerald Associates, a Calgary-based firm that provides project management solutions from Oracle. “They often make decisions without real big data analytics and collaborative tools. But people aren’t always ready for the level of transparency that’s now possible.” Asked why a company would not automatically buy into a solution that would massively help decision-makers, her answer is terse: “Firefighters want glory.”

The suggestion is, of course, that many big data management tools are so powerful that they can dramatically de-risk oil and gas projects. Many problems end up much more predictable and avoidable. As a result, people whose jobs depend on solving those problems and putting out fires see their livelihoods threatened by this IT trend. Resistance and suspicion, always a dark side of any corporate culture, rears its ugly face.

On the other hand, more progressive companies have already embraced the opportunities of big data. They don’t need convincing and have long since moved from resistance to enthusiastic adoption. They have grown shrewder and savvier and base their IT investments very objectively according to cost-benefit metrics. The central question for vendors: “So what’s the ROI?”

There is big confusion about big data, and there are different views about where the oil and gas industry is lagging in terms of adopting cutting-edge tools. Scott Fawcett, director at Alberta Innovates – Technology Futures in Calgary and a former executive at global technology companies like Apptio, SAP SE and Cisco Systems, points out that this is not small potato stuff. “There has been an explosion of data. How are you to deal with all the data coming in in terms of storage, processing, analytics? Companies are spending a lot of money on IT. Suncor alone spends about $500 million per year.” He then adds, “And that’s even at a time when memory costs have plummeted.”


The big data story had its modest beginnings in the 1980s, with the introduction of the first systems that allowed the energy industry to put data in a digital format. Very suddenly, the traditional characteristics of oil and gas and other resource industries – often unfairly snubbed as a field of “hewers of word and carriers of water” – changed fundamentally. The shift was from an analog to a digital business template; operations went high-tech.

It was also the beginning of what The Atlantic writer Jonathan Rauch has called the “new old economy.” With the advent of digitization, innovation accelerated and these innovations cross-fertilized each other in an ever-accelerating positive feedback loop. “Measurement-while-drilling, directional drilling and 3-D seismic imaging not only developed simultaneously but also developed one another,” wrote Rauch. “Higher resolution seismic imaging increased the payoff for accurate drilling, and so companies scrambled to invest in high-tech downhole sensors; power sensors, in turn, increased yields and hence the payoff for expensive directional drilling; and faster, cheaper directional drilling increased the payoff for still higher resolution from 3-D seismic imaging.”

One of the biggest issues in those early days was storage, but when that problem was more or less solved, the industry turned to the next challenge of improving the processing and analysis of the enormous and complex data sets it collects daily. Traditional data applications such as Microsoft Excel were hopelessly inadequate for the task.

In fact, the more data and analytical capacities the industry got, the more it wanted. It wasn’t long ago that E&P companies would evaluate an area and then drill a well. Today, companies still evaluate then drill, but the data collected in real time from the drilling is entered into the system to guide planning for the next well. Learnings are captured and their value compounded immediately. In the process, the volume of collected data mushrooms.

The label “big data” creates confusion, just as does the term Big Oil. The “big” part of big data is widely misunderstood. It is, therefore, helpful to define big data with the three v’s of volume, velocity and variety. With regard to the first “v,” technology analysts International Data Corp. estimated that there were 2.7 zettabytes of data worldwide as of March 2012. A zettabyte equals 1.1 trillion gigabytes. The amount of data in the world doubles each year, and the data in the oil and gas industry, which makes up a non-trivial part of the data universe, keeps flooding in from every juncture along the exploration, production and processing value chain.

Velocity, the second “v,” refers to the speed by which the volume data is accumulating. This is caused by the fact that, in accordance with Moore’s famous law, computational power keeps increasing exponentially, storage costs keep falling and communication and ubiquitous smart technology keep generating more and more information.

“In the old days, people were driving around in trucks, measuring things. Now there are sensors that do that work.”

On the velocity side, Scott Fawcett says, “In the old days people were driving around in trucks, measuring things. Now there are sensors doing that work.” Sensors are everywhere in operations now. Just in their downhole deployment, there are flowmeters and pressure, temperature, vibrations gauges as well as acoustic and electromagnetic sensors.

Big data analytics is the ability to asses and draw rich insights from data sets so decision-makers can better de-risk projects. There is a common big data focus of oil and gas companies on logistics and optimization, according to Dale Sperrazza, general manager Europe and sub-Saharan Africa at Halliburton Landmark. If this focus is too one-sided, companies may end up just optimizing a well drilled in a suboptimal location.

“So while there is great value in big data and advanced analytics for oilfield operations and equipment, no matter if the sand truck shows up on time, drilling times are reduced and logistical delays are absolutely minimized, a poorly chosen well is a poorly performing well,” writes Luther Birdzell in the blog OAG Analytics.

Birdzell goes on to explain that the lack of predictive analytics results in about 25 per cent of the wells in large U.S. resource plays underperforming, at a cost of roughly $10 million per well. After all, if a company fails to have enough trucks to haul away production from a site before a storage facility fills up, then the facility shuts down. Simply put, when a facility is shut down, production is deferred, deferred production is deferred revenue, and deferred revenue can be the kiss of death for companies in fragile financial health.

The application of directional drilling and hydraulic multi-stage fracturing to hydrocarbon-rich source rocks has made the petroleum business vastly more complex, according to the Deloitte white paper The Challenge of Renaissance, and this complexity can only be managed by companies with a real mastery of big data and its analytical tools. The age of easy oil continues to fade out while the new data- and technology-driven age of “hard oil” is taking center stage. The capital costs of unconventional oil and gas plays are now so high and the technical requirements so convoluted, the margins for error have grown very small. Decision-makers can’t afford to make too many bad calls.

Despite the investments companies are putting into data-generating tools like sensors, much of the data is simply discarded, because the right infrastructure is missing. “IT infrastructure should not be confused with just storage; it is rather the capacity to warehouse and model data,” according to Nicole Jardin at Emerald Associates. If the right infrastructure is in place, the sensor-generated data could be deeply analyzed and opportunities ­identified for production, safety or environmental improvements.

Today, operators are even introducing automated controls that register data anomalies and point to the possible imminent occurrence of dangerous events. Behind these automated controls are predictive models which monitor operational processes in real time. They are usually coupled with systems that not only alert companies to issues but also make recommendations to deal with them. Pipelines are obviously investing heavily in these systems, but automated controls are part of a much larger development now sweeping across all industries and broadly called “the Internet of things” or “the industrial Internet.”

“In the ’80s, when data was being stored digitally, it was fragmented with systems that weren’t capable of communicating with each other,” Fawcett says. The next wave in big data is toward the holistic view of data system de-fragmentation and integration. “Ultimately,” Jardin says, “in order to analyze data, you need to federate it. Getting all the parts to speak to each other should now be high priority for competitively minded energy companies.”

Originally posted via “How oil and gas firms are failing to grasp the necessity of Big Data analytics”

Source: How oil and gas firms are failing to grasp the necessity of Big Data analytics by analyticsweekpick

2016 Trends in Big Data Governance: Modeling the Enterprise

A number of changes in the contemporary data landscape have affected the implementation of data governance. The normalization of big data has resulted in a situation in which such deployments are so common that they’re merely considered a standard part of data management. The confluence of technologies largely predicated on big data—cloud, mobile and social—are gaining similar prominence, transforming the expectations of not only customers but business consumers of data.

Consequently, the demands for big data governance are greater than ever, as organizations attempt to implement policies to reflect their corporate values and sate customer needs in a world in which increased regulatory consequences and security breaches are not aberrations.

The most pressing developments for big data governance in 2016 include three dominant themes. Organizations need to enforce it outside the corporate firewalls via the cloud, democratize the level of data stewardship requisite for the burgeoning self-service movement, and provide metadata and semantic consistency to negate the impact of silos while promoting sharing of data across the enterprise.

These objectives are best achieved with a degree of foresight and stringency that provides a renewed emphasis on modeling in its myriad forms. According to TopQuadrant co-founder, executive VP and director of TopBraid Technologies Ralph Hodgson, “What you find is the meaning of data governance is shifting. I sometimes get criticized for saying this, but it’s shifting towards a sense of modeling the enterprise.”

In the Cloud

Perhaps the single most formidable challenge facing big data governance is accounting for the plethora of use cases involving the cloud, which appears tailored for the storage and availability demands of big data deployments. These factors, in conjunction with the analytics options available from third-party providers, make utilizing the cloud more attractive than ever. However, cloud architecture challenges data governance in a number of ways including:

  • Semantic modeling: Each cloud application has its own semantic model. Without dedicated governance measures on the part of an organization, integrating those different models can hinder data’s meaning and its reusability.
  • Service provider models: Additionally, each cloud service provider has its own model which may or may not be congruent with enterprise models for data. Organizations have to account for these models as well as those at the application level.
  • Metadata: Applications and cloud providers also have disparate metadata standards which need to be reconciled. According to Tamr Global Head of Strategy, Operations and Marketing Nidhi Aggarwal, “Seeing the metadata is important from a governance standpoint because you don’t want the data available to anybody. You want the metadata about the data transparent.” Vendor lock-in in the form of proprietary metadata issued by providers and their applications can be a problem too—especially since such metadata can encompass an organization’s so that it effectively belongs to the provider.

Rectifying these issues requires a substantial degree of planning prior to entering into service level agreements. Organizations should consider both current and future integration plans and their ramifications for semantics and metadata, which is part of the basic needs assessment that accompanies any competent governance program. Business input is vital to this process. Methods for addressing these cloud-based points of inconsistency include transformation and writing code, or adopting enterprise-wide semantic models via ontologies, taxonomies, and RDF graphs. The critical element is doing so in a way that involves the provider prior to establishing service.

The Democratization of Data Stewardship

The democratization of big data is responsible for an emergence of what Gartner refers to as ‘citizen stewardship’ in two capital ways. The popularity of data lakes and the availability of data preparation tools with cognitive computing capabilities are empowering end users to assert more control over their data. The result is a shifting from the centralized model of data stewardship (which typically encompassed stewards from both the business and IT, the former in accordance to domains) to a decentralized one in which virtually everyone actually using data plays a role in its stewardship.

Both preparation tools and data lakes herald this movement by giving end users the opportunity to perform data integration. Machine learning technologies inform the former and can identify which data is best integrated with others on an application or domain-wide basis. The celerity of this self-service access and integration to data necessitates that the onus of integrating data in accordance to governance policy falls on the end user. Preparation tools can augment that process by facilitating ETL and other forms of action with machine learning algorithms, which can maintain semantic consistency.

Data lakes equipped with semantic capabilities can facilitate a number of preparation functions from initial data discovery to integration while ensuring the sort of metadata and semantic consistency for proper data governance. Regardless, “if you put data in a data lake, there still has to be some metadata associated with it,” MapR Chief Marketing Officer Jack Norris explained. “You need some sort of schema that’s defined so you can accomplish self-service.”

Metadata and Semantic Consistency

No matter what type of architecture is employed (either cloud or on-premise), consistent metadata and semantics represent the foundation of secure governance once enterprise wide policies based on business objectives are formulated. As noted by Franz CEO Jans Aasman, “That’s usually how people define data governance: all the processes that enable you to have more consistent data”. Perhaps the most thorough means of ensuring consistency in these two aspects of governance involves leveraging a data lake or single repository enriched with semantic technologies. The visual representation of data elements on an RDF graph is accessible for end user consumption, while semantic models based on ontological descriptions of data elements clarify their individual meanings. These models can be mapped to metadata to grant uniformity in this vital aspect of governance and provide semantic consistency on diverse sets of big data.

Alternatively, it is possible to achieve metadata consistency via processes instead of technologies. Doing so is more tenuous, yet perhaps preferable to organizations still utilizing a silo approach among different business domains. Sharing and integrating that data is possible through the means of an enterprise-wide governance council with business membership across those domains, which rigorously defines and monitors metadata attributes so that there is still a common semantic model across units. This approach might behoove less technologically savvy organizations, although the sustainment of such councils could become difficult. Still, this approach results in consistent metadata and semantic models on disparate sets of big data.

Enterprise Modeling

The emphasis on modeling that is reflected in all of these trends substantiates the viewpoint that effective big data governance requires strident modeling. Moreover, it is important to implement at a granular level so that data is able to be reused and maintain its meaning across different technologies, applications, business units, and personnel changes. The degree of prescience and planning required to successfully model the enterprise to ensure governance objectives are met will be at the forefront of governance concerns in 2016, whether organizations are seeking new data management solutions or refining established ones. In this respect, governance is actually the foundation upon which data management rests. According to Cambridge Semantics president Alok Prasad, “Even if you are the CEO, you will not go against your IT department in terms of security and governance. Even if you can get a huge ROI, if the governance and security are not there you will not adopt a solution.”


Originally Posted at: 2016 Trends in Big Data Governance: Modeling the Enterprise

February 13, 2017 Health and Biotech analytics news roundup

News and commentary about health and biotech data:

How data science is transforming cancer treatment scheduling: Scheduling appointments is a problem that many electronic systems are not able to handle efficiently. New holistic approaches inspired by manufacturing are helping to improve this process.

Data Analytics May Keep Cancer Patients out of Emergency Departments: Researchers at the UPenn school of medicine are developing a model to predict when patients need emergency care, as well as best practices for when they need such care.

Why practices are struggling to exchange records: A recent survey found some key issues in the exchange of electronic health records, including a lack of confidence in the technology and difficulties transferring data across different systems.

Pharmacist and drug associations want better data on medicine shortages: The associations called for more up-to-date information about possible shortages from providers.

Secrets of Life in a Spoonful of Blood: Researchers have increasingly powerful tools to study fetal development, including sequencing DNA found in the mother’s blood.

Originally Posted at: February 13, 2017 Health and Biotech analytics news roundup by pstein

How to Use Social Media to Find Customers (Infographic)

How to Use Social Media to Find Customers (Infographic)
How to Use Social Media to Find Customers (Infographic)

Everyone talks about how important it is to be on Social Media.  But how do you use it to gather more customers?  Well, Kathleen Davis has shared some information compiled by Wishpond.


Did you know that 77% of B2C have found new customers through Facebook, while LinkedIn has proven significant for B2B. Try 277% more effective than Facebook. Those are some outstanding numbers!


Check out the rest in the Infographic below.


Lessons Big Data Projects Could Use From Startups




The Pitch Deck We Used To Raise $500,000 For Our Startup

The Pitch Deck We Used To Raise $500,000 For Our Startup
The Pitch Deck We Used To Raise $500,000 For Our Startup

I came across Pitch Deck used by Buffer co-founders Joel Gascoigne and Leo Widrich to raise $500k round. This deck has lots of good information and it could be really useful for other startups seeking to raise capital.

Why it is relevant for most of startups? Because buffer founders were also first timers.

One of the big no-no’s we’ve learnt about early on in Silicon Valley is to publicly share the pitchdeck you’ve used to raise money. At least, not before you’ve been acquired or failed or in any other way been removed from stage. That’s a real shame, we thought. Sharing the actual slidedeck we used (and one, that’s not 10 years old) is by far one of the most useful things for others to learn from. In fact, both Joel and I have privately shared the deck with fledging founders to help them with their fundraising. On top of that, our case study is hopefully uniquely insightful for lots of people. Here is why:
Half a million is not a crazy amount: It’s therefore hopefully an example that helps the widest range of founders trying to raise money.
Both Joel and myself are first-timers: We couldn’t just throw big names onto a slideshow and ride with it. We had to test and change the flow and deck a lot.

As a summary: this deck is build upto one key slide: Traction.

So without further ado – have a look at their pitch deck.

ps: If you want to read more? check out OnStartups.com

Source by v1shal

Best Practices For Building Talent In Analytics

Best practice pinned on noticeboard

Companies across all industries depend more and more on analytics and insights to run their businesses profitably. But, attracting, managing and retaining talented personnel to execute on those strategies remains a challenge. This is not the case for consumer products heavyweight The Procter & Gamble Company (P&G), which has been at the top of its analytics game for 50 years now.

During the 2014 Retail/Consumer Goods Analytics Summit, Glenn Wegryn, retired associate director of analytics for P&G, shared best practices for building the talent capabilities required to ensure success. A leadership council is in charge of sharing analytics best practices across P&G — breaking down silos to make sure the very best talent is being leveraged to solve the company’s most pressing business issues.

So, what are the characteristics of a great data analyst and where can you find them?

“I always look for people with solid quantitative backgrounds because that is the hardest thing to learn on the job,” said Wegryn.

Combine that with mature communication skills and a talent for business acumen and you’ve got the perfect formula for a great data analyst.

When it comes to sourcing analytics, Wegryn says companies have an important strategic decision to make: Do you build it internally, leveraging resources like consultants and universities? Do you buy it from a growing community of technology solution providers? Or, do you adopt a hybrid model?

“Given the explosion of business analytics programs across the country, your organization should find ample opportunities to tap into those resources,” advised Wegryn.

To retain and nurture your organization’s business analysts, Wegryn recommended creating a career path that grows and the importance of encouraging talented personnel internally until they reach a trusted CEO advisory role.

Wegryn also shared key questions an organization should ask to unleash the value of analytics, and suggested that analytics should always start and end with a decision.

“You make a decision in business that leads to action that gleans insights that leads to another decision,” he said. “While the business moves one way, the business analyst works backward in a focused, disciplined and controlled manner.”

Perhaps most importantly, the key to building the talent capability to ensure analytics success came from P&G’s retired chairman, president and CEO Bob McDonald: “… having motivation from the top helps.”

Wegryn agreed: “It really helps when the person at the top of the chain is driven on data.”

The inaugural Retail & Consumer Goods Analytics Summit event was held September 11-12, 2014 at the W Hotel in San Diego, California. The conference featured keynotes from retail and consumer goods leaders, peer-to-peer exchanges and relationship building.

Article originally appeared HERE.

Originally Posted at: Best Practices For Building Talent In Analytics by analyticsweekpick

What Marketers Really Need To Know About Big Data

Big data is a game changer. But is big data a tech or marketing tool? The answer is both. Big data plays a role in marketing campaigns and gathering insight. To utilize big data most effectively, marketers must understand its use in the following roles.


Trends and Predictive Analytics

Salesforce.com highlights the role of Google Trends as a key player. Google Trends takes big data analysis to the big leagues. It cuts out all of the current trending topics and focuses on the ones with the greatest reach, quantifying the frequency of each searched term and comparing it to the total amount of searches. This helps marketers to expand their reach more than ever. It’s easy to get complacent with search words you already know. With tools like Google Trends, you can find new trends and new markets. Think about the growth of video screens in places of worship, for example. Marketers consistently focus on entertainment and office environments that need large video screens. But by using tools like Google trends can you see the demand and need for services outside of your usual targets and grow to meet changing needs and expanding your reach.

Predictive analytics is another big data strategy. Predictive analytics is an area of data mining that extracts information from existing data sets to determine behavior patterns and predict future trends. Forbes talks about its progressive and aggressive nature. It looks at scores of historical data and analyzes it with incredible speeds. It can literally pinpoint the exact process that makes for successful leads. Predictive data allows marketers to learn more about their target customers than ever before. Using predictive data analytics will drastically change your selling cycles. Instead of waiting for your clients to request additional services, you can predict their needs.

Persona Creation

Much like SEO techniques in the past, big data will change the way we create and use buyer personas. Buyer personas are generally use in marketing efforts to pinpoint a certain customer. According to Salesforce.com, companies usually create personas from data gleaned from their websites and from feedback from sales teams and call centers, which misses huge pools of data. Using big data, social media, blog posts, and marketing campaigns can be geared to more specific customers by targeting demographics in much larger ways. A multimarket approach breaks the generic mould. A small business owner will use your services if they see blog posts geared to them; this is the same for other markets like health care and education. By gearing posts to different people, you gain loyalty from a larger group of markets.

Personalization and Customization

Every marketer knows the effectiveness of personalization and customization. When your customers think you are talking to them directly, they become loyal to your brand. It’s also important to make sure you send the right message at the right time to secure customer loyalty. Using big data, you can see who’s interacting with your brand in real time. Armed with this information, marketers can send personalized and customized responses. You can merge big data with existing CRM practices, and keep tabs on trends of potential buyers as well as current customers. As you track these patterns, you can send customers individualized content creating greater customer retention. Let’s say your customer is looking for new services for the latest project they are taking on. By sending personalized content, you create a relationship that makes them feel like you care about their success. This is how you create loyalty.

Marketers who ignore big data, even small data, will be left behind. Don’t let that be you.

To read the original article on HuffPost Business, click here.

Source: What Marketers Really Need To Know About Big Data by analyticsweekpick