The Executive’s Guide to Assessing and Improving Your Data Strategy

improving your data strategy

Is a weak data strategy sabotaging your analytics initiatives?

Artificial Intelligence and its applications are growing in visibility as early adopters publicize their successes. This surge in popularity is highlighting a major problem: leaky and inefficient data strategies.

80% of the world’s created data is stored by enterprises, but only 1% of that information is used to inform business decisions. The rest languishes in data silos or ages out of usefulness before it can be processed.

While these shortcomings aren’t new, the true extent of the problem becomes visible only when companies begin to integrate AI into their workflows. Suddenly they see conflicting data streams for one domain, a single unreliable source for another, and no way to quickly disperse information to the departments who need it.

A solid data strategy is the tool needed to smooth out these wrinkles.

However, 34% of marketers surveyed by the CMO Council last year admitted that their data strategy isn’t embraced throughout the leadership team. 24% have no data strategy at all. They may appoint a CDO but don’t take the additional step of outlining clear directives.

companies that have a formal customer data strategy
Source: CMO Council

This oversight is the first step to failure for analytics initiatives.

Data science is a growing field with a lot of moving parts. There is a clear benefit to integrating it into enterprise workflows, but it can be difficult to know which practices are enterprise-ready and which will add needless complexity to your organization.

This guide will help you evaluate your current data strategy, assess how well it aligns with your corporate goals, and construct a data science plan that will propel your company to the head of its field.

Audit Your Data Strategy

Before anything else happens, conduct an audit of your current data strategy. The more thorough the audit, the more opportunity there is to spot wasted effort and lost opportunities. Use these guided questions to make sure you don’t miss anything vital.

What data do you have and how do you access it?

Identify every method you currently have of collecting information. This includes marketing data, website metrics, feedback collection and analysis procedures- essentially, anything that measures how your company is performing and how consumers are interacting with your brand.

The more detail you use here, the better. Don’t forget to include data from embedded analytics. Even if you haven’t begun adding analytics on your own, most websites that were built within the last decade will have at least some tools that track site activity.

A growing number of social media and marketing apps also offer embedded analytics. Take a look at exactly what is available.

  • Are you using analytics tools to track your users’ activity? Which ones?
  • Can you track an individual’s path through your website, or does your software only collect aggregate data from all users?
  • Do you have defined Key Performance Indicators (KPIs)?
  • How much say do you have in what metrics are tracked? Are you able to customize collection streams to highlight KPIs?
  • Do you have a central dashboard for viewing and manipulating your data? If not, how many programs does a user have to log into when creating reports?

How is data stored?

Cloud storage is the future of data warehousing, but not everyone has made the switch yet. Some companies have in-house systems they’ve invested heavily in, and uploading everything to the cloud doesn’t make sense for them.

There are also “hybrid warehouses” where some incoming data flows get tagged for in-house storage while others are directed to the cloud. Figure out which system your organization uses.

  • How much data do you save? Do you save everything or only data related to KPIs?
  • Are your storage limits imposed by internal decisions or available space?
  • What are your backup procedures? How can you recover your data in the event of a loss?
  • Do you have an employee or contractor assigned to oversee your data storage?
  • How will you know if your data storage fails or becomes corrupted?
  • Who can see your data? What are the security procedures used to keep unauthorized users from using/altering data?

How is your data being used?

What are you doing with your collected data? This isn’t the place to gloss over gaps in current procedures.

Businesses who don’t effectively use their data will be losing $1.2 trillion to their competitors every year by 2020, so if there is room for improvement here it only benefits your organization to point it out.

  • What is your data telling you about clients, market conditions, and workflow efficiency?
  • Are you using AI techniques such as machine learning?
  • Does your data generate actionable insights?
  • Do you act on the insights generated by your data?
  • How are you using data to optimize marketing, increase customer satisfaction, and improve internal processes?

forrester research

Conduct a Needs Assessment

The needs assessment consists of two stages: planning solutions for errors found during the strategy audit and determining what emerging technologies can be most effectively integrated.

Identifying weaknesses

During the assessment some problems with the current data strategy should become apparent. It will be obvious if there is no backup, for example, or if huge amounts of data are currently being unused.

Other issues may take longer to realize. These tend to be workflow problems, workarounds that staff has created in order to function in a dysfunctional data environment.

The two most common are data silos and Shadow IT.

Data silos

A data silo is a collection or storage system that is only accessible to one group within an organization. Drawing data from these systems for use in other places adds several extra layers of work for employees.

Data silos can be formed accidentally when the higher leadership is unaware of a resource one department has and how it can be applied to the company at large.

For this reason, CDOs and CIOs need to cultivate reciprocal relationships with their subordinate managers.

There should be a climate where those managers feel empowered to share their ideas and processes without being accused of “getting bogged down in details”.

This gives C-level execs the chance to see opportunities for applying those processes in other departments.

Every meeting doesn’t need to be a class on how a department runs; an in-depth update once or twice a month is generally sufficient to stay on top of developing procedures.

Sometimes, data silos are intentionally created by IT departments in the interest of security. Security versus flexibility is one of the greatest conflicts in data science.

It’s critical to ensure information (especially protected customer information and strategy-sensitive data) is kept from unauthorized use, but at the same time too many silos make completing even the basic tasks complicated.

Shadow IT

Difficulties in accessing data needed to operate leads to the second most common “data dysfunction”: Shadow IT. This consists of any systems, applications, and procedures adopted by non-IT staff without IT consultation.

Despite its ominous name, Shadow IT isn’t caused by a desire to hurt the company.

Employees become frustrated with inefficient workflows or limited capabilities and act to “fix” those problems.

They install enterprise software (or sometimes write their own) that automates as much of their “housekeeping” tasks as possible in order to give themselves more time to focus on their primary jobs.

Allowing key decision makers to champion new technology has the benefit of increasing flexibility and offloading some of the IT workload. It isn’t without risk, however.

Unapproved software can expose the organization’s data to the security risks the IT manager created the data silo to avoid.

Also, without central coordination resources are wasted on redundant or conflicting software.

For more information about these hazards that IT face, read last month’s blog post: The Dangers of Shadow IT in Mobile App Development.

Evaluating new data science applications

How well is your current data strategy delivering results? If you aren’t using Artificial Intelligence-based analytics software, there’s room for improvement.

AI allows for faster analysis of unstructured data which makes up an estimated 80% of the world’s data.

Including AI in your data strategy is the first step to introducing it into your business strategy. For inspiration, here are some of the technologies being used by top level enterprises today.

Deep learning

Just as machine learning is an application of Artificial Intelligence, deep learning is an sub-discipline of machine learning.

Some industry publications describe it as an evolution, but this is misleading as machine learning is still a vibrant and growing field.

Both machine and deep learning teach software to make increasingly more accurate choices about data based on past experience with little to no human input.

Deep learning focuses more on the creation of deep neural networks: vast collections of data that help refine the program’s definitions of categories.

Imagine a company wants to design a program to screen customer images posted to a social media site for inappropriate content.

A series of initial algorithms is written to define what “inappropriate” means. The program uses these algorithms to approve or flag incoming content.

In the beginning, though, the software will make a lot of mistakes while it attempts to understand the provided instructions. Patterns of color and unusual body positions could cause false positives.

Deep learning shortens the training period by feeding an enormous amount of prepared data through the algorithm during the preparation phase.

Because the program can access a deep pool of pre-screened exemplars to check its results, it doesn’t have the same learning curve as machine learning processes that must construct their own models.

Results returned by deep learning algorithms reach usable levels of accuracy faster.

On an enterprise level, deep learning is most beneficial in cases where a company already has a store of sorted data to apply.

Current applications of the technology include predicting the outcome of legal cases, navigating self-driving cars and guidance systems for the visually impaired, automatically generating reports in response to unstructured triggers (such as the text of a complaint email), and providing more challenging virtual opponents for computer and video games.

Data mining

Data mining and predictive analytics are often used interchangeably, though in reality data mining is a process that powers predictive analytics.

It’s one of the techniques that creates the framework that predictive analytics uses to generate its predictions. Modern applications of data mining use machine learning to refine their output.

There are different categories of data mining depending on the desired end state.

  • Association is used to find connections between events (ie, customers look at these two websites during the same visit).
  • Path analysis is the logical continuation of that process in which the typical order of events is defined (customers look at these FAQ pages before choosing the “Contact” page).
  • Data clustering also groups data by proximity but without assuming causation (for unknown reasons, the most customers come from these six cities).
  • Classification sorts data into classes based on differentiating factors (customers who have made a purchase in the past vs customers who only browse the site).
oracle data mining
Source: Oracle

Data mining helps companies find previously unknown patterns in their data. Opportunities for growth are often overlooked because the data obscures them.

Marketing is the highest profile enterprise application of data mining (determining when and how to implement marketing campaigns) but other departments can take advantage of it as well.

For instance, data mining can serve as a virtual feedback panel for product designers.

Knowing what features of an app users interact with most and which are ignored helps plan updates and refine upcoming products to more accurately align with customer needs.

In a market where 86% of customers will pay more for a better experience, improving responsiveness is a significant competitive edge.

Streaming analytics

Gone are the days when companies could afford to wait for the quarterly sales report to evaluate their performance.

The growth of the online economy has created a constantly shifting environment with opportunities disappearing as quickly as they arise, and organizations without the ability to continually visualize their operational data will lose ground to their better-prepared competitors.

For this reason, streaming analytics is one of the most critical analytics technologies to include when building a data strategy.

Data from multiple sources are analyzed mid-stream before being directed to data warehouses. Executives can check a central dashboard with living graphs and charts, providing a real time snapshot of operations.

Streaming analytics can be used to set off alerts when certain KPIs pass relevant levels.

If response to a certain advertising campaign suddenly spikes in an area, management can assess whether the activity is positive or negative and react accordingly.

Fast and satisfying reactions are key to reducing the impact of errors on public relations.

Examples of this concept are all over the news.

United Airlines is still a source of ridicule for their weak response to misguided staff while American Airlines, who had a similarly publicized incident between a flight attendant and a passenger, was able to react in time to minimize the damage to their reputation.

Look at the difference in search results on each of these events:

united airlines vs american airlines

The difference in media reporting on these two events makes a strong case for being able to quickly evaluate public response based on unstructured data.

In-house data management team vs. Outsourcing

How much of your analytics will be conducted in-house and what will be outsourced?

If you have unusual or very complex requirements, you will need a dedicated team including engineers, programmers, and at least one statistician.

With the rise of embedded analytics in enterprise software, however, investing in data science doesn’t necessarily require engineers and programmers.

Most businesses won’t need a high-science analytics team. A contractor can streamline and coordinate your analytics programs and recommend new software that is a good fit for your company.

Structuring Good Data Strategy

With all this information at hand, it’s time to create the data strategy itself. This can be the most contentious part of the process.

C-level executives with different areas of responsibility have different ideas about what the plan should look like and who should be responsible for its adoption and upkeep.

43% of enterprise leaders feel that getting everyone to agree to the same data strategy is too hard.

In truth, good data strategy prevents more problems than it causes. It removes the uncertainty around data management by outlining expectations of all involved parties.

After adoption of the data strategy different departments of the organization will find it much easier to rely on data coming from other branches since they have more trust in the collection and management procedures.

There is no standard template for enterprise data strategy. Your business is unique, even among your competitors, and what works for another company might not compliment your existing operations.

There are certain elements that every data strategy should address in order to be considered complete.

Data Storage

Before any data is collected, there needs to be a storage system in place. The main decision here is whether to build local storage or contract for cloud storage.

Local storage will often have faster connection speeds, and you will have complete control over functions such as backups and access control.

You can also manually disconnect local storage from the internet in case of a network attack. Setting up local storage comes with a large up-front investment, though. Also, you will have to arrange for maintenance and security personnel to protect that investment.

Cloud storage side-steps the costs of building and managing local servers. The provider handles maintenance and improvements as part of the cost, which is typically structured as a subscription.

It’s possible to purchase more storage as your business grows without being delayed by construction. Keeping data stored on the cloud protects it against on-site accidents, too.

These advantages come at the cost of less control over the details of data storage and a slightly slower connection speed.

The vast majority of businesses won’t notice an appreciable difference in connectivity between local and cloud storage, so for most people cloud storage is the best solution.

Collection and Exploitation

Although collection and exploitation are different domains, the rise of embedded analytics has tied them together.

An increasing number of products that used to simply collect data are now processing it as well, and few companies are willing to invest in new software that doesn’t include some form of analytics.

Planning for collection includes deciding what information you need to track. Be specific, but don’t feel the need to ration your KPIs. Data science needs data to work.

The more relevant information you have, the more ROI you can realize from data science programs. Don’t forget to address gaps in your data infrastructure revealed during the audit stage.

Exploitation covers everything from which embedded analytics programs will be utilized to the new data science applications you plan to adopt.

What do you want your data to do? What goals should your CDO be working towards?

While these will by nature be loosely defined, try to narrow it down more than “growth”. A better exploitation goal would be “increase growth in X market” or “improve the customer acquisition funnel”.

Data Integration

Describe your executive expectations for adoption of data science enterprise-wide. This section should include a detailed plan for how data should be disseminated throughout the company.

Incorporating data science into existing workflows is most efficiently done on a rolling phased basis.

That is, identify the first few steps to improving your data usage, then periodically reassess and add new steps as the old ones are completed.

Provide metrics to help managers assess their data science integration.

A data strategy should be dynamic as well as specific. Make sure there are guidelines for adjusting plans to fit new information, but don’t change requirements on a weekly basis.

Alterations to your strategy should always be data-driven and push towards well-defined goals.

Governance and security

Determine who owns each data asset within the company. Who is responsible for overseeing it? Who can make changes? Who can retrieve data? How will your data be protected from external malicious actors and internal negligence? What measures are in place to comply with relevant privacy laws or HIPAA regulations?

There’s an executive trend towards democratizing data so that it’s accessible by every department.

That provides an incredible amount of flexibility and encourages innovation on an individual level, but there are some security concerns involved.

Decide what level of access each category of employee will have based on what you deem an acceptable balance of risk.

Resist the urge to centralize your entire data governance to the CDO. Assign key data governors at each level of authority, all the way from the CDO to individual departments.

This is a good way to balance freedom of data versus security, in fact.

Each governor can assess their section’s need for specific data more easily than the CDO, and having everything flow through that local governor provides a measure of accountability.

information governance landscape
Source: aiim.org

Conclusion

Sound data strategy and the resulting increase in data utilization translates into profit: Fortune 1000 companies who increase their data utilization by a mere 10% can add up to $65 million to their net income.

By assessing and improving your company’s data strategy, you can position yourself to take advantage of new AI technologies and win a share of that increased profit.

Concepta can help assess your data strategy. Contact us today for a consultation!
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Machine Learning – The New Competitive Advantage for Enterprise Business

developing a machine learning strategy

Machine learning is on track to transform enterprise as we know it.

A recent survey conducted by MIT Technology Review and Google Cloud shows that machine learning has become an IT priority for the vast majority of organizations, with 60% already using the technology to some extent.

Another 18% say they plan to implement it within 12-24 months. Only 5% of respondents reported that their companies weren’t interested in machine learning.

The survey targeted executives and enterprise developers, with some vice president-level leaders mixed in.

Tech firms are well represented, but so are the financial and business industries.

The size of respondents’ organizations is equally diverse: 48% 50 or fewer employees, 22% 50-1000 employees, and 32% 1000 or more employees.

role responsible for machine learning initiatives
Source: MIT Technology Review

Aside from the diversity of respondents, the MITTR survey is particularly interesting because of its comparison of current machine learning users to those planning in implement machine learning in the near future.

Placing the groups side by side provides insight into how ML is typically integrated into an organization’s digital strategy over time.

For example, Machine Learning planners tend to push towards applications such as recommendation engines. 56% of executives list recommendations as a primary goal while creating their Machine Learning strategy, along with text-mining (55%) and personalization (47%).

These are the strategies most likely to generate a sufficient ROI to justify further investment.

Companies with mature ML programs express slightly different priorities. They already have a working strategy to build on, so they can widen their gaze. Their objectives for next year are data security (44%), localization and mapping (43%), and targeted marketing (39%).

ML planners relegate those to their five-year plans.

The two groups share some interests. Predictive planning (53%), automated bots (40%), and smart assistants (37%) were equally popular among all the executives surveyed.

Machine Learning in Practice

Before talking about how machine learning is being used, it’s useful to first make a distinction between ML and Artificial Intelligence.

Artificial Intelligence is an umbrella term. It refers to the idea of a computer (or any machine) behaving in ways a human would consider intelligent.

Machine learning is one of those ways: an application of AI in which programs are encouraged to correct and learn from their own mistakes when exposed to data.

The most profitable enterprise uses of ML involve classifying unstructured data and using it to produce business insights.

Here are a few popular applications, listed with their average rate of adoption from the MITTR survey:

Text mining and emotion analysis (both 47%):

These techniques overlap so often it’s easiest to list them together.

Text mining is the use of machine learning to take chunks of text from emails, social media posts, or other sources and analyze them to determine the subject.

Emotion analysis (also called sentiment analysis) estimates the mood of a text’s writer.

These two disciplines are combined very effectively to produce insight about how customers interact with a brand online as well as accurately directing emails to the proper departments without the need for human interference.

Image recognition, classification, and tagging (47%):

Being able to identify the subject of an unlabeled picture is one of ML’s core strengths.

Imagine an algorithm that could sort and tag incoming pictures of damaged merchandise for insurance claims or detect whether pictures uploaded to a social media page violated community guidelines.

Natural Language Processing (45%):

NLP is the backbone of the advanced chatbots used for online customer service, but that’s not its only enterprise application.

A scientist can sort terabytes of online papers using NLP to create a customized list of suggested sources for their research.

Likewise, the time it takes lawyers to find relevant case precedents is drastically reduced.

Recommendations (42%):

E-commerce brought in $394.9 billion in the United States last year, growing 15.1% from 2015. At the same time the average attention span of an online shopper dropped 30%.

Smart machine learning strategies by companies like Amazon and Netflix have conditioned consumers to expect to find their product they want without scrolling through multiple pages.

Implementing a machine learning-enable recommendations engine is a good first step to holding a shopper’s attention through checkout.

ML programs can also apply affinity analysis to suggest additional items that go along with products already placed in the cart.

Real World Results

The true measure of success is how machine learning performs in the real world. What kind of ROI are early adopters seeing? Can machine learning really deliver on its promises?

The answer to both questions seems to be “yes”. While machine learning has a long way to go before realizing its full potential, it is advanced enough now to grant a significant competitive edge to its users.

Take a look at some of the most common enterprise goals of machine learning programs:

  • Better data analysis and insights (50%)
  • Faster analysis and more timely insight (45%)
  • Improved internal efficiency (39%)
  • Better understanding of customers (35%)
gains from machine learning
Source: MIT Technology Review

When early adopters of machine learning were asked about the actual results they have experienced so far, here were their answers:

  • Better data analysis and insights (45%)
  • Faster analysis and more timely insights (35%)
  • Improved internal efficiency (30%)
  • Better understanding of customers (27%)

The gap between expectation and reality is surprisingly small. It seems more impressive considering that machine learning is showing results faster than many other tech trends.

28% of users with mature ML programs have established ROI, and a third of companies with fledgling ML programs say they’re already showing signs of doing so.

ML users attribute the accelerated profitability in part to increased productivity.

While machine learning does have a learning curve, once the systems are in place they free human employees to focus on higher priority projects.

The lower error rate of ML programs also saves time that would otherwise be spent finding and correcting mistakes.

Moving Forward

A recurring theme throughout the survey is the belief that machine learning will give companies a significant competitive edge over their peers. 26% of current ML users feel it already has for their organization.

Machine learning may be a young field, but results like these show it can stand up to the demands of enterprise.

Could your enterprise business benefit from machine learning services? Contact Concepta to explore your options!

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How Your Company Can Prepare for the Coming AI Revolution

ai revolution

Perhaps “revolution” is too strong a word. Artificial Intelligence (AI) and Machine Learning (ML) applications have been gradually taking on more management responsibilities for years.

That trend is clearly accelerating. That’s why it may appear revolutionary in retrospect, especially to companies left behind.

While the AI revolution is not characterized by a massive and sudden change, it does involve various markets reorganizing themselves around companies that use AI to achieve surges in productivity.

That’s what the World Economic Forum meant when it concluded last year that AI is initiating the “4th Industrial Revolution.”

Here are some of the actions your business can take now to get ahead of the curve.

Specific Areas for Immediate Research

There are several applications that deserve attention in the year ahead. Gartner’s Symposium/ITxpo identified the most significant AI trends that will impact businesses in 2017.

That list includes:

Natural Language Processing (NLP)

This is how customers will interact with apps as keyboards go away.

Devices connected as part of the Internet of Things (IoT) will be too small for any other interface. The smartphone is growing into a universal voice controller for customers to use in controlling their environments.

Blockchain

A company-specific distributed ledger that records transactions from peer-to-peer will make possible a host of new concepts in business services.

This will reset how companies deal with any sort of value transfer, including practices in security, identity verification, accounting, supply chain and equity distribution.

Mesh app and service architecture (MASA)

Omnichannel has been the leading challenge of marketing teams, and MASA is the operational equivalent.

It defines the back-end services required to present customers with a fluid experience as they interact with companies on and offline.

AI in the Years Ahead

The following four areas are where the influence of AI and machine learning will make the drive to be the biggest changes in the years to come.

Those areas are how consumers expect to be treated, what types of upgrades have become standard, how internal business units collaborate, and how projects managers organize their resources.

Each offers businesses a route to incorporate greater operational efficiencies and customer engagement as AI applications evolve.

4 Ways Businesses Can Use AI

Consumer Expectations

Customer-centricity means finding meaningful patterns in customer data and generating new offerings based on that analysis, rather than through traditional product development cycles.

Impressions emerge from conversations in social networks and online path to purchase actions.

Instead of pain points, the new product developers work from what ranks highest in improving relevancy and brand perception.

Service Upgrades

All kinds of devices from watches to thermostats now includes AI software that anticipates behavior and reacts to individual preferences.

Digitization also has come to service offerings like streaming video, predicting what customers will want to watch next.

AI helps manufacturers determine which materials are more durable, when inventories are running low and the priorities of machine maintenance.

Collaboration Across Time Zones

Based on the above two changes, the speed of decision-making has had to increase.

Supply chains have gotten much longer as AI has knocked down barriers in transportation and logistics networks.

Collaboration tech has had to keep up with this speed, establishing a foundation of financial data normalized across currencies and accounting principles.

Organizational Behavior

Global collaborations demand new business models and original ways of coordinating project teams.

Talent sourcing, a more productive company culture and repeatable innovation initiatives have all been enhanced with AI software.

The New Market Leaders

It doesn’t take predictive analytics to see that companies with smarter targeting and lower costs will win their markets.

The industries that have already seen the greatest disruption, such as media, retail, transportation and financial services, are those where AI has vastly increased the speed of information.

In those industries, the new market leaders operate lean organizations that offer customers entirely new channels for getting what they want.

If you want to learn more about AI, contact us today or visit our website.

Four Ways to Overcome Your Company’s Big Data

big data

As predictive analytics and big data become central to the success of modern marketing strategies, you may feel overwhelmed with the variety of new terms being tossed around. But with a big data revolution already in motion, you can begin implementing and understanding some of these tools to propel your business efforts even further.

Here, we’ll separate some commonly confused methods and tools to lay out the purposes and possibilities with each. For the three data essentials your company should be focusing on, check out our other post: What’s the Best Way to Visualize Your Data?

Business Intelligence

When you think of business intelligence (BI) you might think of simple data collection. But true business intelligence involves transforming data into useful guidance. Thankfully, BI is no longer a set of unknown tools existing only for mega-corporations who can afford it. Essentially, BI tools offer answers to big questions that matter through reports, KPIs, and trend-reviewing.

BI software allows companies to gather their data into one program, rather than juggling several lower-capability tools like Access and Excel. BI software typically stores data in warehouses, allowing for easier collaboration and collective decision-making within a company.

Download our eBook if you would like to learn how to implement business intelligence in your company.

Data Science

On the surface, data science may sound identical to business intelligence, but there are several differences – the most important being that BI provides data visually, while data science extracts data to gain more insight.

Using data science, you can uncover the significance of questions that you didn’t even think to ask. You might also discover rich information, like what kinds of content are most likely to go viral, ways to optimize emails or your rate of customer churn. Instead of being warehoused, data can be shared in real-time.

Data science opens up unlimited opportunities, as businesses can conduct more experimental research and delve into new markets of which they were previously unaware.

Predictive Analysis

Predictive analytics is a branch of data science. It can strengthen your ability to understand customers and tweak your decision-making to be more accurate. Companies can finally have a data-informed strategy for out-performing competitors.

The specific power of predictive analysis is that it gives companies insight into relationships and correlation – does A impact B, and how? While you may not prove causation, the simple finding of a correlation can guide strategic moves and ultimately boost your sales, audience size, and more.

Machine Learning

Many mistake machine learning (ML) for artificial intelligence. However, the purpose of ML is not to create technology with advanced cognition. The purpose is to streamline the process of solving certain business problems. Using mathematical and statistical algorithms, ML allows businesses to use data to fine-tune marketing campaigns for maximum effectiveness.

While it may seem that ML is a brand new function in digital marketing, examples of it can be found in many common places – Google’s “did you mean” capability that corrects spelling errors is just one example.

One factor you must keep in mind is that the quality of your data greatly impacts the usefulness of ML tools. Many predict that ML will enable a much higher capacity for “data storytelling,” answering many of the “why’s” that businesses couldn’t determine before.

While you may not grasp each and every concept in big data just yet, or know precisely how and when to utilize every tool, you can begin a slow immersion — one that will eventually lead you to harness more information, make better decisions and build a stronger business.

For more information on these methods and tools, simply reach out to us or visit our website.

Smart Data Series: An Introduction to Machine Learning

machine learning

To conclude our Smart Data Series, we are going to talk about machine learning. If you missed any of the topics in this series, you can visit them below:

Machine learning has turned the business software market upside down. Instead of users telling business software what to do, the software now tells the user what to do. Those who don’t quickly adopt this smarter, more predictive software risk falling behind.

What Is Machine Learning?

Traditional software strictly follows a set sequence. While outcomes might vary based on the input, the trigger for each outcome is preset within the code. Given the exact same inputs, the outcome will always be the same unless a human changes the code. Machine learning, on the other hand, automatically adapts its program to reach the desired outcome.

Consider the example of spam filtering. A traditional filter is programmed to look for specific keywords, email addresses or patterns. If it isn’t achieving the desired results, the programmer would need to manually adjust the filter.

A machine-learning spam filter might start with those initial parameters, but it would continually adapt. When a user flags a spam email that got through the filter or marks a flagged email as “not spam,” the machine-learning filter compares the characteristics of those emails and adjusts its parameters to try to get similar emails in the right place without user intervention.

While both a human and a machine can theoretically reach the same results, the machine can process large quantities of data more quickly and implement immediate changes without the need to wait for a software update. Additionally, while hard-coded software is generally one size fits all, machine learning can adapt to the preferences and habits of individual users.

Machine Learning and Data Science

Machine learning derives from data science, the practice of extracting knowledge from large sets of data. These large data sets can be used to train machine learning algorithms. In turn, machine learning has widened the possible range of applications of data science, as it increases the range of situations in which a machine can be used to draw conclusions from data. Algorithms can often analyze data much more quickly than even the most highly skilled human, speeding up many data analysis tasks.

Business Applications of Machine Learning

While the big data trend goes back decades, machine learning opens data analysis into new areas. Instead of big corporations having to make budget decisions about where to allocate teams of analysts and small companies being left out of the game, machine learning can be integrated into virtually any piece of software. Here are just a few examples of applications.

Inventory and Pricing

Retail stores and manufacturers are faced with constant decisions about how much inventory to order and when to reduce prices to avoid unsold inventory. Infinite factors can influence demand, including what nearby competitors do, the local economy, weather and demand for complementary items.

While pricing and inventory analysts have long built models to analyze these trends, machine learning can use past data to calculate infinite scenarios and build more accurate predictions.

Quality Control

A business’s reputation can be tarnished long before customer complaints reach its executives. Airbnb found this out after reports of discriminatory actions by its hosts surfaced.

Machine learning can look for small patterns and red flags that could indicate potential problems before the customer is even impacted. This allows companies to maintain high levels of quality and proactively deal with any problems that do arise.

Fraud Detection

Machine learning can also turn areas like consumer or employee fraud into issues that can be dealt with proactively rather than reactively. Transactions that fall outside of typical patterns can be flagged for manual review.

If the transaction turns out to be fraudulent, greater emphasis is placed on similar patterns in the future. If there was a non-fraud explanation, the software can adapt to avoid future false alarms without ignoring real frauds.

As machine learning continues to grow, the question will shift even more clearly from “How can you afford machine learning” to “How can you afford not to use machine learning?”

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