5 Ways to Build Internal Support for Your BI Initiative

business-intelligence

Business intelligence may have transformative potential, but it’s also a significant investment.

Too often, that investment goes unrewarded. Last year Gartner found that 70% of corporate business intelligence initiatives fail before reaching ROI.

Even when projects succeed, they are used by less than half of the team.

The lesson to be learned from this isn’t to avoid business intelligence, though. There’s too much to be gained from using data to build a dynamic, factual model of operations and customers.

Instead, executives should address one of the root causes of BI failure: internal resistance and a general lack of adoption.

Try these approaches to build team support for business intelligence.

Use Success Stories to Build Enthusiasm

Employees have a full set of regular duties to handle. Learning and using business intelligence adds more to their slate.

A well-designed system will save them time and effort once established, but they need to be motivated to put in the effort to learn new tools.

Business intelligence seems like an esoteric concept to some. It can be hard to see a direct connection between data and results.

Instead of throwing out dry statistics, frame business intelligence in terms of what it can do for the team using real examples.

Before early initiatives, find success stories from competitors or comparable organizations. Use those to build excitement for the upcoming project.

Once each phase of the business intelligence project is finished the results can be marketed to the internal team to keep that positive momentum going.

When pitching business intelligence to the team, keep reviews specific but short. Choose clear metrics that demonstrate the actual effects of the project without getting bogged down in details.

For example: “Sales teams closed 23% more contracts last quarter using the new lead management system.”

Integrate BI into Daily Workflows

There’s no incentive to change if staff can default to the old system. People get comfortable in a routine, even when it isn’t effective.

They prefer to stick to what they know rather than learn new procedures.

Nudge resistant team members out of their rut by removing the option to use old systems whenever possible.

Don’t disrupt everything at once, but do have a schedule for phasing out old tools and switching to new ones. Publicize the schedule so it isn’t a surprise when old programs won’t open.

At the same time, make it easy to adopt business intelligence.

Be sure users are properly trained on the new tools, to include putting reference materials where they can be easily accessed by everyone.

Sometimes resistance stems from embarrassment or unfamiliarity, so also refrain from criticizing team members who need extra training or refer to training material frequently.

Create Business Solutions, not just High-Tech Tools

Misalignment between business needs and tool function is a leading reason for lack of adoption.

IT gets an idea for something they can build to collect new data, but it isn’t geared towards an actual business goal.

The product becomes busy work that distracts staff from core functions.

Business intelligence tools need to address specific pain points order for the team to use them.

They should have a clear purpose with an established connection to existing business goals. It’s also important that the new tool is demonstrably better than the current system.

If the tool takes ten minutes to update every day and the old system took five minutes twice a week, it won’t be adopted.

Along the same lines, favor simplicity in function and design. Don’t build an overly complicated multi-tier system only engineers can understand.

Aim for a unified dashboard with intuitive controls and a straightforward troubleshooting process.

Remember that the Team are Vital Stakeholders

Finally, don’t overlook the value of employees as stakeholders in any business intelligence initiative.

They have “on the ground” knowledge of internal operations that can guide the creation of a more targeted system. Take advantage of their expertise early in the development process.

Include key internal team members when gathering stakeholder input during discovery.

Go beyond management and choose representatives from the groups who will use the tools after release. Solicit and give serious attention to team feedback, both during and after release.

Bringing the team in from the beginning does more than build better software. It creates a company-wide sense of ownership.

When team members feel they had a hand in creating business intelligence tools, they become enthusiastic adopters.

Build Support, Not Resentment

Above all, keep the process positive. Encouraging adoption of business intelligence doesn’t have to be a battle of wills.

Focus on potential gains, not punishment for failing to fall in line. Bring the end users in early, listen to their feedback, and build a system that helps them as much as it helps the company.

When the team is excited – or at least convinced of the product’s value – they’re much more likely to adopt business intelligence in the long run.

Every level of operations can benefit from business intelligence. If you have a project in mind, we can help make a compelling case for BI that encourages everyone to get on board. Sit down with one of our experienced developers to find out more!

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Data Quality Checklist: Is Your Data Ready for Business Intelligence?

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To get the most from a BI investment, make sure the data pipeline is in order first.

There’s an old saying that is often applied to analytics: “Garbage in, garbage out.” Results are only as good as the data which feeds them. In fact, preparing that data is 80% of the analytics process. Taking shortcuts with data quality is a fast way to undercut business intelligence efforts.

This checklist is a useful guide for evaluating the existing process and making plans for future infrastructure.

Why is Data Preparation Important?

Data comes in many formats, especially when coming from different sources. When everything is funneled into a communal database there may be blank fields, differences in field labels and numbers, and variations in numerical formats that read differently to a computer (dates are one example of this). Depending on the databases similar records may be duplicated or merged into a single entry.

Messy input like this can produce null or even misleading results. When the data can’t be trusted, it negates the advantage of business intelligence. Data has to be organized into a consistent format before analysis.

Data Quality Checklist

There are five key aspects of good data. To be useful, it should be:

Complete

There must be enough data to warrant analysis. All critical fields should be full and there should be an acceptable percentage of non-critical fields filled in as well.

Accurate

Data should be validated and come from a reliable source. “Reliable” has different meanings based on the type of data, so use good judgement when it comes to choosing sources. Consider who owns or manages the source as well as how the data is collected.

Relevant

Low cost cloud storage has enabled businesses to store more data than ever before. That can be an advantage- as long as it can potentially be used to answer business questions. Also, check whether the data is still current or if there’s more up-to-date data available.

Consistently structured

Prepare data for analysis in an appropriate format (such as CSV). Data scraped from PDFs and other file types may be in an unstructured state that needs more work to be usable. Follow common text and numerical conventions. Currency and dates, for example, are noted differently in the US versus Europe. Check for duplicates and contradictory data as well; this is a common issue when importing from different sources.

Accessible

All concerned end users should be able to access the company’s data, providing it’s legal and ethical for them to do so (for example, HIPAA records should be protected). Make sure this can happen in real or near-real time; when staff has to wait days for data requests to come back they tend to move ahead with less informed choices instead.

Make sure there’s a designated data steward who is empowered to maintain the data pipeline. It doesn’t have to be a separate position, but they should be able to speak to company leadership when there’s an issue.

Think in terms of “data lakes” as opposed to “data silos”, too. Data lakes put the entirely of the company’s data in the hands of those looking for innovative ways to improve operations. They can make decisions based on all available information without worrying that some hidden bit of data might derail their plans. (Automaker Nissan has seen great success from this strategy.)

Options for Data Preparation

When it comes to data preparation, the options boil down to manual versus automated techniques.

Manual data preparation is when employees go through data to check its accuracy, reconcile conflicts, and structure it for analytics. It’s suitable for small batches of data or when there are unusual data requirements, but the labor investment is high.

Benefits

  • Less obvious investment (labor goes up instead of a technology outlay)
  • Low training burden
  • Granular control
  • In-house data security

Limitations

  • Slow
  • Staff could be working on more high-value tasks which are harder to automate
  • Prone to human error
  • Expensive when labor is considered

With automated data preparation, software is used to sort, validate, and arrange data before analysis. Automation can handle large datasets and near real-time processing.

Benefits

  • Fast enough to prepare data for streaming analytics
  • Highly accurate
  • Removes labor burden
  • Works on both the front and back end of collection

Limitations

  • Staff must be trained on the software
  • Initial investment required
  • Working with outside vendors requires extra vigilance for security purposes

Final Thoughts

Data quality may be the least exciting part of business intelligence, but it’s the only way to get reliable results. Take the time to build a strong foundations for your data intelligence process and you’ll be rewarded with more reliable, better-targeted insights.

Having doubts about your data quality? Set up a free consultation with Concepta to assess where you are in the business intelligence process and how to get where you’re going.

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The Easiest Way to Implement Business Intelligence For Enterprise

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The benefits of business intelligence are clear to see. Using data makes companies more efficient and highly agile, positioning them to take advantage of opportunities as they arise instead of racing to keep up with the competition.

What isn’t so obvious is how to make the shift towards making data-driven decisions. There are so many BI tools on the market that deciding where to start can seem overwhelming.

The easiest way to stay focused is to build around specific business goals rather than choosing a trendy tool and trying to make it fit. Having a roadmap and a destination keeps business intelligence efforts on track, even when making adjustments as needs evolve.

Every roadmap will be different, but there are some guidelines every company can use to put together a practical, effective business intelligence plan.

Get Your “Data House” in Order

It can’t be said too often that business intelligence is only as good as the data feeding it. Bad data turns into flawed analysis, which leads to wasted time and money.

The first step of any business intelligence project should be conducting a comprehensive assessment of the company’s current data situation. Be sure to include:

  • Data sources available for use
  • Current data management practices
  • Potential stakeholders in a business intelligence project (both major and minor)
  • Wishlist for data or analytics capabilities

The goal is to clarify what the company has now and what would best help push performance to the next level.

This is also a good time to recommit on a company level to good data management. Business intelligence leads to a stronger flow of incoming data, and having familiar policies in place early will help staff take it in stride.

Work in Phases

Set a list of priorities and work in self-contained, cumulative phases to spread business intelligence across the organization. It may be tempting to just start fresh with a whole new system, but there are two compelling reasons to favor a modular approach.

Cost

So much goes into launching a business intelligence initiative. The costs go beyond buying or building software. Companies must also consider the cost of integrating it into their existing workflows and improving the data pipelines that feed the analytics.

Starting small both reduces the initial investment and allows the benefits of early projects to help pay for later ones.

Building support

One of the biggest killers of business intelligence projects is a lack of internal adoption. Maybe the product doesn’t fit into existing workflows, or staff aren’t convinced of its benefits.

It doesn’t help that sales teams for BI solutions tend to oversell their software. As a result executives expect too much, too soon, and when the desired results don’t materialize on schedule they become disenchanted.

A phased adoption plan allows the first success stories to build excitement for the business intelligence process. It serves to help manage expectations. Everyone can see how the first project played out and knows what they stand to gain.

Some areas show results more quickly than others, making them better choices for building support. For example, it’s easy to demonstrate the value of email marketing analytics or intelligent customer profiling and lead scoring. Both make staff’s jobs easier while noticeably increasing revenue.

Start with Market Tools

Don’t rush to build business intelligence software from the ground up right away. Needs may be unclear in the beginning; only thorough experience will companies discover does and doesn’t work. It can be frustrating to realize an expensive new suite of software requires an equally expensive overhaul of related workflows.

There are plenty of analytics tools and software on the market to experiment with while getting a feel for business intelligence. Options like Google Analytics, Salesforce, MailChimp, and User Voice offer an impressive suite of tools powerful enough to see real results.

As these prove their worth, companies can have custom software built to organize the various data streams into customized dashboards. These dashboards bridge the gap between the moment when companies are getting all the analytics they need but managing the results is too unwieldy and the point where their needs can only be met with a fully custom solution.

Evaluate, Adjust, Reassess

Schedule periodic assessments to review the business intelligence process as a whole.  Get feedback from all stakeholders, including weighing adoption rates by department to check for inconsistencies that could signal a problem.

Measure performance results against meaningful yardsticks. It’s not enough to say something general like, “Reports increased by 60%”.

Instead, assess the actual impact on productivity and budget with specific instances: Time spent managing leads dropped by 35% while successful sales calls increased by 15%.”

Business intelligence is a dynamic process. Remember to leave room for adjustments going forward. Look back on previous phases to evaluate their long-term value. How are they integrating with new technology? Have they met expectations, or is their performance trailing off?

Don’t be afraid to replace a component that doesn’t work. It’s important to give tools enough time to show ROI, but that doesn’t mean sticking with solutions that are causing problems.

This constant evaluation and correction process is the key to staying on the business intelligence roadmap without getting caught up in costly detours.

What can business intelligence do for you? How can you work BI tools into your workflows in a way that makes sense? To get recommendations about business intelligence software and learn how to organize your data into insights that drive real-world revenue, set up a free consultation with Concepta today!

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The 4 Biggest BI Trends Fueling Enterprise in 2018

2018 Business Iintelligence Trends for enterprise

This year, businesses will be striving to keep pace with the digital revolution while maintaining focus on their core business.

The biggest business intelligence trends of 2018 are those that let companies solve problems at the lowest level, work wherever the market takes them, and pivot to meet new opportunities as they arise.

Shift to Self-Service BI

Under self service BI, a company’s existing employees conduct business intelligence activities instead of hiring trained analysts.

This used to be a very chancy strategy, but it’s becoming more feasible as BI software designed for non-analysts grows in popularity.

Self-service BI products give non-data scientists a way to usefully prepare and interpret data without going through IT.

Executives can pull data on their schedule instead of waiting on an IT help ticket. This both lightens the IT workload and increases agility.

It cuts through confusion, too. Users can specify exactly what they need and know whether the results are helpful or not.

As an added bonus the convenience of this approach gives a significant boost to technology adoption rates.

The trend is gaining momentum worldwide. The global self-service BI market is predicted to reach $7.31 billion by 2021.

Some innovative tools are leading the charge in 2018:

  • Augmented data preparation allows business users controlled access to company data for testing theories. These users can clean, reformat, and adjust data as needed using intuitive tools.
  • Smart data discovery tools can query data without formal programming languages, making information widely accessible. The software is generally “drag and drop” based to reduce the learning curve.
  • Embedded analytics provide quick, easy-to-view analysis of commonly used KPI. They were big in 2017, but in 2018 they’ll get smarter and more user-friendly.

Emphasis on Data Visualization

Data visualization is the art and science of translating data into a visual format.

It goes beyond making pretty charts for meetings; often the same data can be interpreted different ways depending on how it’s displayed.

Visual-based data sees much more use than text. It’s easier to understand and absorb, reveals patterns that aren’t visible in raw data, and highlights outliers.

More importantly, it holds viewers’ attention and starts conversations.

Infographics are among the most shared types of enterprise media.

In the past visualization was mainly static (like printable charts and graphs). This year will see a rise in the dynamic visualization of streaming analytics.

Rise in Mobile BI

Enterprise apps make up a growing portion of mobile usage, and a majority of high-level companies are adopting BYOD (bring your own device) policies.

People are already using their phones for both work and play, so it was only a matter of time until BI also made the jump to mobile.

Executives don’t spend all their time at desks anymore. They’re frequently in motion, moving around the company or going out to get eyes on critical processes.

Mobile analytics tools put data where executives need it, when they need it.

Modern smartphones generally have plenty of power and memory available to run enterprise apps, too.

Utilizing that power keeps technology costs down while simultaneously being more convenient for employees.

2018 will bring more mobile-specific analytics solutions, not just interfaces for existing software.

Developers will focus on customizable dashboards and visualizations that make the most of smaller screens.

Growth of Multi-Cloud Solutions

Multi-cloud strategy refers to the practice of incorporating more than one cloud service (like Amazon Web Services, Microsoft Azure, or the Google Cloud Platform) into software architecture.

A distributed architecture like this has advantages over the single-cloud structure.

  • Cost: Developers can choose the most cost-effective solution for each component.
  • Security: Multi-cloud solutions offer more transparency, and it’s easier to oversee the environment as a whole.
  • Agility: It’s easier to upgrade to newer and better technologies as they mature.

85% of companies operating in the cloud favor multi-cloud solutions, using 8 different clouds on average.

Look for this number to rise through 2018 as more companies refine their cloud strategies.

Is your BI strategy ready for 2018? Concepta can streamline your business intelligence strategy and create dynamic visualizations to make your data more accessible. Contact us for a free consultation!

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Separating Machine Learning from Data Science

machine learning vs data science

The enterprise applications of machine learning are weaving themselves into the fabric of everyday business.

Still, the concept itself is hazily understood.

Over the last month we have shared posts intended to clear up the confusion between machine learning and other related topics like predictive analytics.

This article continues that trend by tackling one of the least helpful misapplications: when machine learning and data science are mistaken for each other.

Laying the Groundwork

Machine learning is a branch of artificial intelligence where, instead of writing a specific formula to produce a desired outcome, an algorithm “learns” the model through trial and error.

It uses what it learns to refine itself as new data becomes available.

Data Science is an umbrella term that includes everything needed to extract meaningful insights from data (gathering, scrubbing and preparing, analyzing, forming predictions) in order to answer questions or make predictions.

It includes areas like:

  • Data mining: The process of examining large amounts of data to find meaningful patterns
  • Data scrubbing: Finding and correcting incomplete, unformatted, or otherwise flawed data within a database
  • ETL (Extract, Transform, Load): a collective term for the process of pulling data from one database and importing it into another
  • Statistics: Collecting and analyzing large amount of numerical data, particularly to establish the quantifiable likelihood of a given occurrence
  • Data visualization: Presenting data in a visual format (charts, graphs, etc) to make it easier to understand and spot patterns
  • Analytics: A multidisciplinary field that revolves around the systematic analysis of data

What Falls Under the “Data Science Umbrella?”

“Data scientists are kind of like the new Renaissance folks, because data science is inherently multidisciplinary.”

Those words from John Foreman, MailChimp’s VP of Product Management, sum up the problem with trying to draw the boundaries of data science.

It’s a vast concept, describing intent more than a specific discipline.

There are, however, four fields generally agreed to cover the majority of data science where they intersect: mathematics, computer science, domain expertise, and communications.

  • Mathematics: Mathematics forms the core of data science. Data scientists need to know enough math to choose and refine the models they use in analysis, especially if they plan to work in machine learning. Understanding the math behind their formulas gives them the ability spot errors and weigh the significance of results.Also, while there are some data points that can be easily read without a heavy math background (conversions, website views, engagement rates, etc), others require specialized knowledge to understand. For example, time series data is very common in business intelligence but hard for casual users to interpret.Mathematical subdisciplines often studied by data scientists include:
    • Statistics (including multivariate testing, cross-validation, probability)
    • Linear Algebra
    • Calculus
  • Computer science: Data science may be older than computers, but the powerful effect of the digital revolution can’t be denied. Computers let data scientists process vast amounts of data and perform incredibly complex calculations at a speed that allows data to be used within a reasonable timeframe.Some of the areas where computer science intersects with data science:
    • System design optimization
    • Cleaning/scrubbing data
    • Graph theory and distributed architectures
    • Programming databases
    • Artificial Intelligence and machine learning
  • Domain knowledge: Data science is a targeted practice. It’s used to generate insights about some specific topic. The data has to be contextualized before it can be put to use, and doing so effectively requires an in-depth knowledge of that topic.Today data science is being applied in nearly every domain. Perhaps some of the most interesting uses can be found in fields like business and health care.
    • Health care
      • Data-driven preventative health care
      • Disease modeling and predicting outbreaks
      • Improving diagnostic techniques
      • DNA sequencing and genomic technologies
    • Business intelligence
  • Communications: Communications is often forgotten when discussing data science, but communication is relevant at nearly every stage of the data science process. It’s a critical link between theory and practice. Data has little value unless it can be applied to solve problems or answer questions, and it can’t be applied until someone other than the data scientist understands it. On the flip side of that statement, data scientists need to know what questions they’re trying to answer in order to choose the best analytical strategies.Though communications are often grouped with domain knowledge, it’s helpful to separate them to emphasize their importance. Here are a few data science-oriented applications of communications:
    • Data science evangelism (spreading awareness about the uses of data science)
    • Clarifying what is needed/desired from data
    • Presenting results in a useful way
    • Data visualization (graphs, charts, models)

The Data Science Process

If separating data science into the above disciplines were easy, though, it wouldn’t be its own field.

In reality each discipline is woven throughout the process with a large degree of flexibility in the combination of techniques used.

Here’s a general, very broad-scope view of the data science process and the disciplines that affect each stage.

  1. Data is collected and stored. Computer science
  2. Questions are asked. (What is needed from the data? What problems does the user hope to solve?) Communications, Domain knowledge
  3. Data is cleaned and prepared for analysis. Math, Computer science
  4. Data enrichment takes place. (Do you have enough data? How can it be improved?) Computer science, Math, Communications, Domain knowledge
  5. A data scientist decides which algorithms and methods of analysis will best answer the question or solve the problem. Math, Computer science
  6. Data is analyzed via Artificial Intelligence/machine learning, statistical modeling, or another method. Math, Computer science
  7. The results are measured and evaluated for value/merit. Math
  8. The validated results are brought to the end user. Communication, possibly computer science
  9. The end user applies the results of data science to real-world business problems. Business, communication

This list is mainly intended to demonstrate how inextricably combined the component disciplines of data science are in practice.

The data science process is never as straightforward as this; rather, it’s highly iterative. Some of these steps may be repeated many times.

Depending on the results, the scientist might even return to an earlier step and start over.

Where the Confusion Lies

After reading this far, the reasons for the confusion between data science and machine learning have likely become clear.

Machine learning is a method for doing data science more efficiently, so it’s misunderstood to be a direct subdiscipline of data science.

In fact, looking at a list of things data science can accomplish reads like a pitch list for adopting machine learning.

Here are a few common data science applications to illustrate the point:

  • Forecasting/predicting future values
  • Classification and segmentation
  • Scoring and ranking
  • Making recommendations
  • Pattern detection and grouping
  • Detecting anomalies
  • Recognition (image, text, audio, video, facial, …)
  • Generating actionable insights
  • Automation
  • Optimization

The reason for this overlap is that machine learning algorithms are very effective tools for sorting and classifying data.

That makes machine learning popular among data scientists, but it doesn’t have the inherent direction and sense of purpose of data science as a whole.

In simple terms: machine learning is a tool, data science is a field of practice.

Machine Learning Isn’t Necessary for Data Science…

While ML is an efficient way of performing data science, it’s not always the best solution. Sometimes it isn’t needed at all. Two notable cases when machine learning is the wrong tool for a job:

  • The problem can be solved using set formulas or rules. If there’s no interpretation needed and context doesn’t change the data, a mathematical model alone can handle the matter. There’s no point in spending resources on machine learning. It might lead to faster results if there’s a large amount of data, but it won’t produce “better” results.
  • There isn’t a massive amount of data involved. This is a case where machine learning does more harm than good. Machine learning requires data, the more the better. Without a store of prepared data to train the algorithm, it can produce unreliable results. Worse, training on a small or unrepresentative sample yields biased results. When there isn’t enough relevant data on a subject to fuel machine learning, other methods of data science are better options for finding answers.

But It Is a Game-changing Advantage.

Despite these limitations, machine learning offers such a distinct advantage that easy to see why data scientists are adopting it in such large numbers.

There are three main situations where it’s generally the best data science method:

  • There’s too much data for a human expert to process. Some data is perishable. By the time a team of human analysts works through it (even using standard computing methods) it’s aged out of usefulness. Other times data is flowing into a system faster than it can be processed. Machine learning algorithms thrive on massive amounts of data. They improve by processing data, so results actually become more accurate over time.
  • There is ambiguity in the ruleset. Machine learning has a long way to go before it can match the human potential for coping with uncertainty and inconsistency, but it’s made huge strides in drawing meaningful results from ambiguous data.
  • Programming a specific solution isn’t practical. Sometimes the code needed to program a solution is so big that doing so would be inefficient. In these cases, machine learning can be used to streamline the analysis process.

The Bottom Line

It’s definitely possible to do data science without incorporating machine learning.

However, the pace of data production is growing every day.

By 2020, 1.7 megabytes of data will be created every second per living person.

Most of that will be unstructured data.

Machine learning is the best tool for dealing with that volume and quality of data, so it’s likely to be used in data science for the foreseeable future.

How well is your company taking advantage of its data? Contact Concepta to learn how we can turn your data into actionable insights!


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How Good Data Visualization Can Transform Your Business

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In the race to find more and better sources of enterprise data, one fact is often overlooked: even the best analytics are useless if no one understands the results.

Data is most effective when integrated into every level of decision-making, but it’s unrealistic to expect every employee to wade through dense analytics reports on top of their other duties.

Instead, data needs to be converted into an accessible format that’s easy to read, use, and share.

This is where data visualization comes into play.

You can learn more about data visualization here:
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Data visualization is the process of taking complex information and displaying it graphically.

It can be as simple as a scatter chart or as detailed as a multi-component infographic.

Some digital graphics feature interactive elements that supply more detailed data.

Whatever form it takes, data visualization has a powerfully transformative effect on business when properly done.

It can:

Simplify large or complex data sets

Processing unstructured data is a core strength of machine learning, but explaining the results is hard to do in plain text.

For example, a program that analyzes social media traffic to determine the overall sentiment towards a brand might create a hundred page report in a single week.

The relevant information from that report could be expressed in a few well-constructed graphics instead.

Uncover new relationships between data sets

Data visualization goes farther than the trend charts marketers have been using for decades.

Being able to see and visually manipulate data provides insight into how unrelated factors are related, particularly which business conditions tend to affect other conditions and which have no bearing on each other.

Some of these relationships are visible only through data.

Help decision-makers absorb data faster

The human brain recognizes images in as little as 13 milliseconds, twice as fast as text.

Factors such as color, shape, and element orientation can add layers of complexity to a graphic without significantly increasing the time necessary to understand.

Meetings where data is presented graphically are therefore faster and more productive.

Suggest future courses of action

Data visualization puts disparate pieces of information in context.

Developing pain points become clearer, giving executives the chance to solve problems before they disrupt operations.

Good visualization also highlights opportunities for growth discovered during analysis.

Boost long-term data recall

Pictures are easier to remember than words.

People remember ten percent of what they hear, but thirty percent of what they see.

A statistic illustrated in a graphic will stick with readers longer than one from a report, no matter how well explained it was.

Engage personnel in data utilization

Organizational resistance is one of the biggest hurdles to overcome when incorporating analytics into the decision-making cycle.

Executives recognize the value of data science but find it cumbersome to use on a daily basis.

Data that’s presented in an engaging visual format is more likely to be used than data sent out in a plain text report.

Encourage Facts, not Feelings

Though intuition has its place in business, statistics show that companies who make the majority of their choices based on gut instincts see a 5% drop in revenue when compared to data-centric companies.

That’s a steep price to pay for forgetting to make data accessible.

Prioritize data visualization and put your insights where they do the most good: in the hands of your leadership team.

For more on this business intelligence topic, read What’s the Best Way to Visualize Your Data?

Let Concepta show you how we can use Power BI to better visualize your data. Contact us today for a free consultation!

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