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 to Introduce AI into Your Business Strategy

artificial intelligence business strategy

It’s no secret that Artificial Intelligence has moved out of the lab and into the boardroom. 64% of senior decision makers believe their organization’s future growth is dependent on AI technologies.

76% report that AI is a fundamental part of their corporate strategy. As IBM CFO Martin Schroeter says, “The debate about whether artificial intelligence is real is over, and we’re getting to work to solve real business problems.”

Solving “real business problems” starts with figuring out how to work Artificial Intelligence into an enterprise’s business strategy.

Executives who’ve heard that AI can help grow their business often struggle with where to start. It’s one thing to read an exciting article about AI online and another to leverage that technology into a competitive advantage.

For those looking to make a case for AI integration, the first step is identifying the issues the company is experiencing (declining customer satisfaction, workplace inefficiency, stagnating growth, etc.).

From there you can explore how AI would address those problems to realize a return, either in profit or expanded capabilities.

What solutions can AI offer your company?

To get the most out of the Artificial Intelligence movement, it’s critical to outline how it will be used in your particular organization. AI isn’t a broad-use tool; it works best when applied to a specific problem.

At its current stage of maturity, these are the most practical applications of Artificial Intelligence.

Automating repetitive tasks

There’s a lot of minutia that goes along with business. Employees spend up to 15 hours a week on administrative tasks like updating tracking spreadsheets, sending follow-up emails, and sorting leads.

It reduces employee satisfaction while wasting high-value labor on low-skill tasks.

Automation is an area where AI really shines.

Andrew Ng, Chief Scientist of Chinese-American web services giant Baidu, created the “one second rule”: if a typical human can do a task with less than one second of thought (meaning the thought needed to determine whether action is necessary, not the time needed to complete the action), AI can automate it faster and more accurately.

In case that sounds underwhelming, here’s a sample of tasks an AI can do with that second using machine learning techniques:

  • Sort an incoming business lead into the proper category
  • Post to a social media network
  • Decide if a forum comment violates community guidelines
  • Check whether a form has been filled in correctly
  • Identify a strange behavior or pattern (such as suspicious purchase activity)
  • Update a spreadsheet

Nine out of ten employees agree that automating these repetitive tasks would make them more productive.

By reducing time spent on necessary but tedious tasks, automation also increases employee satisfaction and (by extension) retention.

Improving the customer experience

The internet has changed how consumers interact with enterprise. People prefer to conduct their business online whenever possible, citing convenience as their primary motivation.

Gartner predicts that by 2020 consumers will manage as much as 85% of their enterprise interactions without speaking to a human.

The same report reveals that 89% of businesses will compete based mainly on customer experience within the next few years.

Automated natural language-based interfaces- called “Intelligent Assistants” when used in enterprise- are fast becoming the standard in all-hours customer care.

IAs can handle most basic online services a customer might need. They suggest products based on current and past activity, help customers navigate a quote process, schedule appointments, or even detect whether a customer’s problem can more easily be solved by a human representative and transfer the chat.

As a result, customers finish their business faster when using an IA than previous limited systems. They’re also more likely to make impulse purchases or upgrades since the suggestions are tailored to them.

70% of consumers will pay more for a hassle-free experience, so if they have a good experience through the IA they’re likely to stay even when prices trend higher.

The superior experience offered by modern IAs is wearing away at old prejudices against “talking to robots” caused by awkward rule-based historical interfaces.

Dan Miller of Opus Research estimates that continued positive customer experiences will completely eradicate that bias, saying “Within three years, enterprise Intelligent Assistants will be the primary point of contact to support real world commerce in the digital realm.”

Generating Timely Business Insights

Predictive analytics are among the most useful AI tools available. Once the sole domain of data scientists, analytics are now often embedded in other software to provide an extra layer of functionality.

Information about marketing campaigns, equipment function, social media activity, and more can be analyzed in real time and presented in a format accessible to the non-technically inclined.

Accessibility is a huge step forward for analytics, considering that the old model involved waiting for a specialist to translate data into usable graphs.

Two years ago only 51% of decision makers felt they could interpret their enterprise analytics without assistance. In 2017 that number is 66% and rising.

Having analytics that executives can read themselves boosts flexibility and allows for faster reactions to industry changes.

Concerns about incorporating AI into business strategy

How practical is AI for enterprise right now?

Some applications of Artificial Intelligence are more mature than others, but the above are all comfortably enterprise-ready.

In fact, predictive analytics and automation are almost necessary for companies looking to grow quickly.

Will AI replace human workers?

There’s a lot of controversy over whether Artificial Intelligence will lead to a massive rise in unemployment, but that doesn’t seem to be where enterprise is trending.

The majority of employers are more interested in AI for efficiency than payroll-reduction.

80% of early AI adopters say they plan to retrain or reassign staff replaced by Artificial Intelligence rather than letting them go.

How much AI is “enough”?

There are a few AI applications that every modern organization should be using (i.e., predictive analytics) but otherwise this has to be answered on a case by case basis.

AI should make things easier, not more complicated. If adding it creates too much complexity, consulting with a specialist can help realign your AI solutions with your organizational goals.

Are you wondering how AI can be added to your IT infrastructure? Concepta can help! 

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Top 4 Ways CIOs Can Help CEOs with Their Digital Business Strategy

cio ceo business meeting

Digital transformation is rocking the foundation of industries across the globe. Big data, cloud computing, intelligent networks, Internet of Things (IoT), mobile devices, robotics and sensors are changing the way consumers interact with businesses and, in turn, how companies provide value to customers.

As your role as CIO becomes the primary adviser to the CEO on strategy, planning and implementation of technology, it is more important than ever.

In fact, the 2016 Gartner CEO Perspectives study, a survey of hundreds of CEOs, reported that a majority of the current top 10 CEO priorities are related to digitization initiatives. And a recent survey of CIOs at a Wall Street Journal CIO Network meeting revealed 70 percent of CIOs aspire to be CEOs.

With that in mind, here are four ways CIOs can help CEOs with digital business strategies and tactics in the months ahead.

After you read this, check out the Top 10 CIO Technology Priorities in 2017.

Share Digital Knowledge

The Gartner report showed that CEOs often garner much of their digital knowledge from peers in other industries.

This gives you the opportunity to coach them on how those concepts apply to your business and industry. CEOs will always be the leader in organizational change, but applying the right digital strategy requires deeper knowledge than most CEOs possess.

You can fill in the gaps for them while getting the resources and tools you need to manage the transformation.

Drive Innovation

Almost 60 percent of CEOs in market-leading firms actively pursue innovation rather than settle for gradual improvements, per a recent IBM report.

As CIO, you can help the CEO determine which digital technologies provide the best opportunity to innovate new ideas, iterate them faster and turn them into real-world gains in speed, efficiency and cost reduction.

You are best-equipped to delineate the optimal solutions to manage digital disruption and alter how the business operates on a daily basis.

Enlist Help to Improve Cybersecurity

Technological change is great, but often it seems to be racing ahead of the ability to keep it safe and secure. The Gartner study found that almost half of CEOs feel cybersecurity should be managed by the CIO rather than business departments.

As CIO, you are positioned to educate them on the issue and show the responsibility for technology security is a shared challenge.

Ray Kelly, an intelligence expert and former commissioner of the NYPD, said cyber is a part of every possible attack vector on corporations today, from conventional crime to political unrest in remote office locations. He notes that all managers, from the CIO to the CEO, must work on security together. Otherwise it gets moved down the corporate priority list, potentially putting the organization at risk.

Bridge the Digital Skills Gap

While recommending and implementing a variety of new technologies is part of your job, getting end users to use these tools is a different challenge.

Go ON is a digital skills charity in the UK dedicated to helping people develop basic digital skills. It counts 12 million people that lack digital skills to grow and prosper in today’s technological world. If current workers don’t have the requisite digital skills, training becomes critical to profitability.

A recent survey from Burning Glass Technologies reveals that digital is affecting “middle-skilled” jobs. These are jobs where applicants have more than a high school diploma but have not acquired a college degree.

The report showed that jobs in this sector requiring digital fluency are expanding much faster than non-technical jobs. Help the CEO determine where training dollars are best spent to ramp up your company’s digital skills.

As CIO, you have a lot more on your plate. But it’s also an excellent opportunity to help the CEO shape how your organization adapts to succeed in the new digital era.

If you need help getting started…
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