#Marketers beware: Privacy Please: How the GDPR Can Elevate Marketing

The EU’s new General Data Protection Regulation (GDPR) can be daunting, but it can have a positive impact on marketing. Here’s why.
— Read on insights.newscred.com/gdpr/

Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

7 examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

Marketing Transformation and Artificial Intelligence

Artificial Intelligence and Marketing
Artificial Intelligence and Marketing

Here are seven examples where artificial intelligence is transforming marketing:

1. Content curation

Predictive analytics allows Netflix to optimize its recommendations. This kind of clustering algorithm is continually improving suggestions, allowing users to make the most of their subscription.

Uniting information from diverse datasets is a common use of AI.

Under Armour is one of the many companies to have worked with IBM’s Watson. The sports apparel company combines user data from its Record app with third-party data and research on fitness, nutrition etc.

The result is the ability for the brand to offer up relevant (personalized) training and lifecycle advice based on aggregated wisdom.

2. Search

In 2015, Google admitted it was using RankBrain, an AI system, to interpret a ‘very large fraction’ of search queries. RankBrain utilizes natural language processing (NLP) to help find relevance in content and queries, as well as better interpretation of voice search and user context (e.g. Google Now).

3. Predictive customer service

Knowing how a customer might get in touch and for what reason is obviously valuable information.

Not only does it allow for planning of resource (do we have enough people on the phones?) but also allows personalization of communications.

Another project being tested at USAA uses this technique. It involves an AI technology built by Saffron, now a division of Intel.

Analyzing thousands of factors allows the matching of broad patterns of customer behavior to those of individual members.

4. Ad targeting

As Andrew Ng, Chief Scientist at Baidu Research, tells Wired, “Deep learning [is] able to handle more signal for better detection of trends in user behavior. Serving ads is basically running a recommendation engine, which deep learning does well.”

Optimizing bids for advertisers, algorithms will achieve the best cost per acquisition (CPA) from the available inventory.

When it comes to targeting of programmatic ads, machine learning helps to increase the likelihood a user will click. This might be optimizing what product mix to display when retargeting, or what ad copy to use for what demographics.

5. Customer segmentation

Plugging first- and third-party data into a clustering algorithm, then using the results in a CRM or customer experience system is a burgeoning use of machine learning.

Companies such as AgilOne are allowing marketers to optimize email and website communications, continually learning from user behavior.

6. Sales forecasting

Conversion management again, but this time using inbound communication.

Much like predictive customer service, inbound emails can be analyzed and appropriate action taken based on past behaviors and conversions.

Should a response be sent, a meeting invite, an alert created, or the lead disqualified altogether? Machine learning can help with this filtering process.

7. Image recognition

Google Photos allows you to search your photos for ‘cats’. Facebook recognizes faces, as does Snapchat Face Swap.

Perhaps the most exciting implementation of image recognition is DuLight from Baidu…Designed for the visually impaired, this early prototype recognizes what is in front of the wearer and then describes it back to them.

Data Driven V. Predictive Marketing: BEWARE JETSON’S MARKETING!

The Big Willowby: Charlie Tarzian

My son came to me one day in early December and said:  ‘Hey, Dad, let’s get Mom one of those robotic vacuum cleaners.  You know, the ones you switch on and they vacuum your whole floor!’  He could not contain his enthusiasm – this was going to be great – no one would have to vacuum our floors ever again!!!

So we went to Amazon (of course) and two days later our round disc of a maid showed up via FEDEX.

Come Christmas Day, the robot fully charged, off we went to the kitchen to marvel at what was certain to be a life changing event.  We turned it on and put it down on the floor and the vacuum swung into action.  It crossed the floor, sensed it was coming to a wall, made a pivot, chugged in another direction…and got stuck on the slight incline by the fireplace…then stuck again on the floor mat by the stove…then got caught between a chair and a table and went into a break dance that would make R2D2 jealous.

I bring this up because a colleague sent me this little snippet from the website of a Predictive Marketing vendor:

“Predictive Marketing doesn’t need to be a services heavy engagement to get you up and running. With CompanyX (name of company withheld) and our push button integration, we can integrate with your existing systems and build your predictive model in under a day. – See more at: http://www.companyx.com/what-we-offer/#sthash.vBzkU18n.dpuf

There you have it: Jetson’s Marketing – just push our one little button and off you go:  great leads, great meetings, great website experiences – in fact all your marketing/sales problems solved in ‘under a day’.  All that is left to do is fire your staff, except for that one person in charge of pushing the button when you run out of leads, meetings and website visitors.

Look, I know what I don’t know, but I can tell you this: whatever you’re thinking the new generation of transformative marketing is – one thing it isn’t is automated bliss.  It takes a fair sized village to make things happen.  And herein lies the huge disconnect between data driven marketers and the shiny new object called Predictive Marketing.  Data driven marketers know that data can and should be utilized across the marketing/sales continuum – but it is more about data orchestration than anything else.  Therefore, one button, add water and stir does not take into consideration any of the cause and effect across all the communications and transactive channels that marketers rely on.

Marketing is services heavy (sorry, Company X) because at the nexus of MarTech, AdTech and Sales Enablement sits quite a bit of cause and effect.  And unless you aspire and build towards using predictive data to positively impact all channels aligning as one – then what you are predicting is a very small sliver of what could be.  In other words, if the connectivity and synapsis among outward facing channels are not orchestrated and optimized using predictive data and you are not feeling good that all channels are working in sync – then how can you predict a scaled outcome?  The predictions you are making will reflect a small percentage of the whole – and so instead of widening your funnel and increasing your opportunities along every step in a buying journey, you are narrowing that funnel based on a flawed assumption that you are predicting against a full boat of reliable data.

On the other hand, Data Driven Marketing  sets up to be based on solving for the cause and effect of what is less than optimized (can anyone say, broken?)  It attempts to determine (not predict) what works and doesn’t and then – as a village – cohesively knits together a response to results that can be repeatable but certainly is not a just add water, one button pushed result.

So – are we starting to see a difference:  Predictive Marketing – a push button approach to a complicated set of executional events and response, or, Data Driven Marketing – a human driven (sorry robots!) approach to the cause and effect of humans communicating to other humans about things that may or may not be important to the recipient (we always hope for the former)?

What do you think?  We would love to know.  Have any stories to share – we would love to hear from you.

by: Charlie Tarzian, Founder, The Big Willow

How Big Data Will Shape Industry in 2016 | Datafloq

how-big-data-will-shape-the-it-industry-in-2016Accurately predicting how Internet trends and future technology will play out is no easy task. This year alone there has been an increase in cyber threats and hacks, new software from Windows and the introduction of smart watches, and of course, a spike in big data. As technology advances, so does its growth for possibilities.

Below are six sure-fire predictions made by analysts for 2016:

1. A Huge Decline in Legacy Vendors

As indicated by a report released by the IDC, almost a third (30 percent) of IT vendors will cease to exist by 2020. As it appears now, many legacy vendors, especially larger ones, will either have to shut down completely, downgrade or partner with another company due to sluggish growth and lost earnings.

Good examples of this trend can be seen with Dell which is rumored to buy out EMC, and HP splitting its operations in half. In a nutshell, legacy vendors as a whole are missing the mark when it comes to delivering practical solutions in the tech industry—a red flag that many vendors will eventually end up being archived in exchange for private equity.

2. Appearance of More Wearables

Within the next two years, wearable health and fitness tracking devices will take the critical role workforce by storm. By 2018, it is estimated two million people will be required to wear health and fitness tracking devices as a safety measure. This includes firefighters, law enforcement, paramedics, remote field workers, airline pilots, industrial workers, professional athletes, and even political leaders.

This expected boom in wearable tech calls for IT professionals that are adept in device discovery. Network and device discovery at this level usually calls for a more formal network device discovery platform, and IT professionals that understand how to implement it.

3. Big Data Gets Bigger

In 2016, big data will have an even greater impact on how many industries function. Diverse industries are seeing the benefit of analyzing large amounts of data from healthcare to language translation. Understandably, more and more companies are adopting big data services and customizations; they’re catching on that utilizing insights based on algorithms is a much more practical strategy to successful marketing and business expansion as opposed to trial and error. By the year 2018, analytics will be embedded in every application to enhance functionality or convenience.

4. Cloud Providers Will Diminish

In reaction to the big data explosion, many big cloud providers will be vying for the chance to host big data storage. Google, Microsoft, and AWS all provide machine learning services as well as access to a range of massive data groups that can be used for analytics. Major public cloud providers will gain momentum and strength, with Amazon, IBM SoftLayer, and Microsoft grabbing a large percentage of the business cloud services market.

Unfortunately, smaller cloud service providers just won’t be able to invest in hosting machine learning services; this will likely be the catalyst for such companies to bail out of the market altogether. The volume of options for cloud management software and general infrastructure-as-a-service (IaaS) cloud services will significantly decline at the end of 2016.

5. More Business Content Generated by Machines

Technologies possessing the ability to proactively assemble and send information through automated composition engines will take a more active role. Business content such as legal documents, market reports, shareholder reports, press releases, articles and white papers will be generated more frequently by machines.

This shift in operations will increase by 20 percent in 2018, this includes machine learning in the IT sector as a whole. The initial companies predicted to expedite and implement smart machine technologies effectively will be startups and other fresh-out-the box companies.

6. Increase of Artificial Intelligence Implementation

According to esteemed analyst Daryl Plummer, the artificial intelligence (AI) trend is the result of enterprises and consumers willingly embracing the advancement of machine-driven technologies. Since the capability of applying smart technology for specific tasks dramatically improves the time, cost and energy generally contributed to recruiting, hiring, training and expansion demands associated with human labor, it’s little wonder, then, that artificial intelligence will play a larger role in a company’s infrastructure in 2016.

Source: Datafloq – How Big Data Will Shape the IT Industry in 2016

Article Author:

Xander SchofieldFollow Xander SchofieldLinkedIn Xander Schofield

How Big Data Will Shape the IT Industry in 2016 | Datafloq

how-big-data-will-shape-the-it-industry-in-2016Accurately predicting how Internet trends and future technology will play out is no easy task. This year alone there has been an increase in cyber threats and hacks, new software from Windows and the introduction of smart watches, and of course, a spike in big data. As technology advances, so does its growth for possibilities.

Below are six sure-fire predictions made by analysts for 2016:

1. A Huge Decline in Legacy Vendors

As indicated by a report released by the IDC, almost a third (30 percent) of IT vendors will cease to exist by 2020. As it appears now, many legacy vendors, especially larger ones, will either have to shut down completely, downgrade or partner with another company due to sluggish growth and lost earnings.

Good examples of this trend can be seen with Dell which is rumored to buy out EMC, and HP splitting its operations in half. In a nutshell, legacy vendors as a whole are missing the mark when it comes to delivering practical solutions in the tech industry—a red flag that many vendors will eventually end up being archived in exchange for private equity.

2. Appearance of More Wearables

Within the next two years, wearable health and fitness tracking devices will take the critical role workforce by storm. By 2018, it is estimated two million people will be required to wear health and fitness tracking devices as a safety measure. This includes firefighters, law enforcement, paramedics, remote field workers, airline pilots, industrial workers, professional athletes, and even political leaders.

This expected boom in wearable tech calls for IT professionals that are adept in device discovery. Network and device discovery at this level usually calls for a more formal network device discovery platform, and IT professionals that understand how to implement it.

3. Big Data Gets Bigger

In 2016, big data will have an even greater impact on how many industries function. Diverse industries are seeing the benefit of analyzing large amounts of data from healthcare to language translation. Understandably, more and more companies are adopting big data services and customizations; they’re catching on that utilizing insights based on algorithms is a much more practical strategy to successful marketing and business expansion as opposed to trial and error. By the year 2018, analytics will be embedded in every application to enhance functionality or convenience.

4. Cloud Providers Will Diminish

In reaction to the big data explosion, many big cloud providers will be vying for the chance to host big data storage. Google, Microsoft, and AWS all provide machine learning services as well as access to a range of massive data groups that can be used for analytics. Major public cloud providers will gain momentum and strength, with Amazon, IBM SoftLayer, and Microsoft grabbing a large percentage of the business cloud services market.

Unfortunately, smaller cloud service providers just won’t be able to invest in hosting machine learning services; this will likely be the catalyst for such companies to bail out of the market altogether. The volume of options for cloud management software and general infrastructure-as-a-service (IaaS) cloud services will significantly decline at the end of 2016.

5. More Business Content Generated by Machines

Technologies possessing the ability to proactively assemble and send information through automated composition engines will take a more active role. Business content such as legal documents, market reports, shareholder reports, press releases, articles and white papers will be generated more frequently by machines.

This shift in operations will increase by 20 percent in 2018, this includes machine learning in the IT sector as a whole. The initial companies predicted to expedite and implement smart machine technologies effectively will be startups and other fresh-out-the box companies.

6. Increase of Artificial Intelligence Implementation

According to esteemed analyst Daryl Plummer, the artificial intelligence (AI) trend is the result of enterprises and consumers willingly embracing the advancement of machine-driven technologies. Since the capability of applying smart technology for specific tasks dramatically improves the time, cost and energy generally contributed to recruiting, hiring, training and expansion demands associated with human labor, it’s little wonder, then, that artificial intelligence will play a larger role in a company’s infrastructure in 2016.

Source: Datafloq – How Big Data Will Shape the IT Industry in 2016

Article Author:

Xander SchofieldFollow Xander SchofieldLinkedIn Xander Schofield

Big Data Trends | Strategies driving investments in data

imgresLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments

Europe’s Top Digital-Privacy Watchdog Zeros In on U.S. Tech Giants – The New York Times

NY TimesPARIS — The latest standoff between Europe and American tech companies runs through a quiet street just north of the Louvre Museum, past chic cafes and part of the French national library, to the ornate office of Isabelle Falque-Pierrotin.

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From here, Ms. Falque-Pierrotin has emerged as one of the most important watchdogs for how companies like Facebook and Google handle the billions of digital bits of personal data — like names, dates and contacts — routinely collected on Europeans. Since 2011, she has been France’s top privacy regulator, and for the last two years, she has led a group of European data-protection officials. In those posts, Ms. Falque-Pierrotin has regularly agitated companies to better safeguard people’s data.

Her role will come into even sharper focus in the coming weeks. Ms. Falque-Pierrotin, empowered by Europe’s highest court, will be at the heart of efforts to police how digital data is transferred outside of the European Union, a central aspect of many European and American businesses. That role will be amplified even further if, as is now widely expected, American and European negotiators fail to reach a new data-transferring deal by Feb.

Read more: Europe’s Top Digital-Privacy Watchdog Zeros In on U.S. Tech Giants – The New York Times

How More Accessible Information Is Forcing B2B Sales to Adapt

jan16-05-563960997by: Andris A. Zoltners, PK Sinha, and Sally E. Lorimer

Over the past 20 years, information technology and digital channels have changed the way consumers shop for products ranging from cars to homes to electronics. Those forces are dramatically changing the way B2B companies and their customers approach buying and selling, too.

Business buyers are more connected and informed than ever before. Sellers must respond. For buyers and sellers alike, this creates complexity, anxiety, and opportunity all at the same time.

From the buyer’s perspective, information technology and digital channels provide access to information and enable self-sufficiency. When a buyer wants to learn about virtually any product or service, an internet search yields thousands (if not millions) of results, including online articles, videos, white papers, blogs, and social media posts. In addition to supplier websites that showcase specific solutions, there are likely to be online sources (ranging from the self-serving to the unbiased) to help buyers learn and compare solution alternatives. Buyers can also use self-service digital channels for new or repeat purchases and for training and support. Using information technology and digital channels, buyers can take over many steps of buying that salespeople once cherished as their source of value.

Buyers are at different levels of self-sufficiency: any single buyer can be at one level for some purchases and at a different level for others. Sometimes buyers prefer to eliminate the salesperson completely. According to one corporate technology buyer: “Our supplier’s customized self-service purchasing portal makes it easy to place reorders, track shipping, and return products hassle-free.” Other times buyers seek help from salespeople. The same corporate buyer relies on salespeople when evaluating new technologies: “It’s more efficient to work with a few trusted salespeople, compared to spending hours on my own sifting through all the information and misinformation that’s out there.”

Because of the diversity of buyer self-sufficiency, the traditional methods sellers use to customize their selling approach for customers are no longer enough. Considering factors such as customer potential and needs is still relevant. But today, customer knowledge/self-sufficiency is a growing driver of how customers want to buy. At one end of the spectrum are the “super-expert” customers, skilled in gathering information from many sources and self-sufficient in using that information to make purchase decisions. At the other end of the spectrum are the “information-seeking” customers, who want help with examining and evaluating the plethora of information. Many customers are in between these two extremes, or are at different points at different times or for different purchases.

Smart sellers match their selling approach to the customer’s level of buying knowledge and self-sufficiency. For example, when leaders at Dow Corning observed in the early 2000s that some customers wanted an easier, more affordable way to buy standard silicone products, they created Xiameter, a brand that includes thousands of less-differentiated products sold exclusively through a low-cost, no-frills, self-service online sales channel. Customers who desired a higher-touch approach could still purchase products under the Dow Corning brand name, which also includes specialty silicones backed by research and technical services.

As sellers need a more customized approach to reaching customers, they have a big arsenal of data and technology at their disposal. Systems (e.g., CRM), tools (e.g., data management, analytics), infrastructures (e.g., mobile, cloud), and information (e.g., big data) give sellers knowledge about buyers and enable sales force members to make smarter decisions. And sellers who once connected with customers primarily through personal selling can now use an array of digital communication channels to supplement or supplant face-to-face sales efforts.

Consider the impact of information technology and digital channels from the seller’s perspective. Here are examples from several industries.

  • Finding banking customers: “Social media allows us to cost-effectively reach out to more prospects and showcase our services.
  • Understanding specialty chemicals customers: “Big data and analytics help us improve customer targeting and achieve more cost-effective deployment.”
  • Acquiring advertising customers: “We now have richer demographic information to help us create more powerful sales messages, resulting in more sales.”
  • Serving and growing business logistics customers: “Our salespeople use a business review app to guide quarterly account reviews with major customers. By sharing data about performance and cost savings, these discussions enhance customer value and retention.

Information technology and digital channels can help sellers become more effective and efficient, but they can also be a source of disharmony and confusion if implemented without thought. Too many sellers have wasted millions of dollars on sales technologies such as CRM systems and data warehouses that never lived up to their potential.

Success for sellers requires many sales force changes beyond information technology and digital solutions. To start, salespeople need new competencies. Customers are no longer interested in meeting with “talking brochures,” so salespeople must do more than share product information. They must adapt to each customer’s level of knowledge and self-sufficiency. They must use email, social media, webinars, video conferencing, and other tools judiciously to maximize their own productivity and make things more efficient for buyers. They must help their companies coordinate customer outreach across multiple communication channels to ensure buyers get a well-orchestrated and consistent message.

For example, in the pharmaceutical industry, gone are the days when the majority of physician education occurred through face-to-face contact between salespeople and physicians. Companies are now tracking individual physician communication preferences and are reaching out with the combination of face-to-face visits and/or digital methods (e.g., websites, email, podcasts, virtual detailing, video conferencing, mobile apps) that best meets each physician’s needs. Salespeople need competencies as orchestrators who can ensure an effective and efficient connection.

Developing new sales force competencies is just a start. Sales leaders must also reengineer their sales forces by implementing changes across the entire range of sales force decisions: roles, size and structure, hiring, training, coaching, incentive compensation, performance management, and sales support systems.

Source: HBR: How More Accessible Information Is Forcing B2B Sales to Adapt

 

Big Data Trends | Strategies driving investments in data

IDG Enterprise logoLast year, IDG published a study 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments that was based on interviews with 1,139 IT leaders from nine industries with high tech (16%), government (12%), financial services (11%) and manufacturing (9%) being the top four industries surveyed.

 

Key findings Infographic:

Below are a few key take-aways, the report is embedded at the bottom of this post:

  • 80% of enterprises surveyed have data-driven and big data projects in implementing or planning stages today versus 63% of SMBs. 37% of enterprises have deployed data-driven projects in the last year, and 18% are in the process of implementing or piloting projects as of today.
  • 83% of organizations prioritized structured data initiatives as critical or high priority in 2015, and 36% increased their budgets for data-driven initiatives.
  • Improving the quality of decision making (61%), improving planning and forecasting (57%) and increasing the speed of decision making (51%) are the three most common business goals and objectives driving data-driven initiatives in organizations today. The following graphic compares which business initiatives are driving big data investment and the positive impact of big data on each.
  • 36% of enterprises expect their IT budget allocations for data-driven initiatives increased in 2015, 41% anticipated budget levels would remain at current levels and 21% aren’t sure. Only 3% say data-driven and big data-related project funding will decrease.
  • Data analytics continues to accelerate as the most preferred solution for gaining greater business insight and value from data, with this category increasing in importance 55% from 2014 survey results. In enterprises, data analytics (65%), visual dashboards (47%), data mining (43%), data warehousing (40%) and data quality (39%) are the five most preferred solutions. In my discussions with CIOs in financial services and manufacturing companies, the shift away from pre-built dashboards with common metrics and key performance indicators (KPIs) to the flexibility of defining their own data models in metrics is the future. Dashboards in financial institutions need to have the flexibility of quickly integrating entire new metrics and KPIs as their business models change. For manufacturers, the need for interpreting shop floor data to financial results is what’s driving data analysis and dashboards in the many manufacturing industries adopting analytics today.
  • The number of enterprises who have deployed/implemented data-driven projects increased 125% in the last year, with 42% still planning data implementations as of today. The following graphic from the study illustrates a comparison of 2014 and 2015 plans for considering, planning and implementing data-driven projects.

 

View the report here:

Download the IDG Report: 2015 Big Data and Analytics, Insights into Initiatives and Strategies Driving Data Investments