Which Business Analytics Trends Can Be Put To Use Today?

business analytics trends

Originally published April 6, 2017, updated Dec. 18, 2018.

The BI technologies which offer the best chance of success today are those that allow companies to take advantage of time-sensitive opportunities while providing more responsive customer service.  

One of the most important parts of developing a digital strategy is knowing when not to jump on a high-tech bandwagon.

Some technologies show potential in small-scale trials but haven’t had enough real-world usage to prove their worth to enterprise. Adopting too early puts companies at risk of losing their investment.

On the other hand, waiting too long leaves them in their competition’s shadow.

There’s a lot at stake in this balancing act. Digital transformation isn’t a luxury anymore. It’s critical for companies who want to stay competitive.

Even chains of three or four locations can fall behind their peers if they aren’t maximizing their data usage.

Of course, building up digital infrastructure costs money. Choosing the right technology is the best way to ensure a smooth return on that investment.

A number of business analytics trends are already picking up speed coming into 2019.

Some are years away from being able to deliver on their promises. Others have reached the stage where a company can reliably use them to gain a competitive edge while side-stepping the risks inherent to early adoption.

The best of this second group are outlined below. These are the trends to adopt for enterprises seeking to improve their data agility.

Predictive analytics

Predictive analytics as a field has existed since the late 1600s, when Lloyd’s of London used it to estimate insurance rates on seagoing vessels.

Until the rise of computers, though, it wasn’t a practical means of steering business.

There were too many variables for a human to consider in time to form more than broad predictions.

Widely available cloud storage and increased processing power changed that.

The field has seen a resurgence as the most efficient way to maximize data usage and feed a data-driven decision making process.

73% of companies consider themselves to be analytically driven, and predictive analytics are behind the most successful of these.

Predictive analytics detect deviations in patterns, generate insights based on evolving activity, and predict future outcomes from gathered data.

The benefits of predictive analytics are clearly demonstrated by the variety of practical applications in use today. One unexpected example is human resources.

Retaining experienced workers is a constant challenge for employers who must cope with turnover rates of nearly 20% (averaged across US industries).

The tech sector suffers from even higher turnover. Replacing lost workers can cost up as much as half their annual salary, not counting lost productivity during the training process.

Using predictive analytics, HR managers can find patterns in their employment data that highlight the reasons good employees leave and suggest the incentives most likely to make them stay: higher salaries, additional training, more appealing benefits packages, or in some cases transfers to more engaging positions.

The data also predicts which employees are most desirable to hire and retain.

There’s still a long way to go before the full potential of predictive analytics is realized. That said, the technology is maturing much faster than experts predicted.

Its current capabilities are more than reliable enough to justify making an investment.

Real time analytics

Real time analytics (also known as streaming analytics) give enterprises an up-to-date visualization of their operations.

It was a growing trend back in 2017, and today it’s living up to that promise.

In the traditional analytics model, information is stored in a data warehouse before analysis is applied.

This causes a gap between collection and results where perishable opportunities are lost.

There’s no rule that says data has to be stored first. It can be analyzed mid-stream to sift out data that will only stay relevant for a short time.

Companies then have the chance to make the most of the opportunity through swift action.

Information gathered by real-time analytics is usually displayed in a dynamic graphic format that doesn’t require a data science degree to understand, too. That makes it easy to act on quickly.

A business that can spot opportunities in time to take action makes much greater use than those left playing catch-up on trends.

The one caveat about streaming analytics is that they work best in data-driven cultures. Be sure to provide both technical training and executive support when launching a real-time analytics tool.

Chatbots and Natural Language Processing

Natural Language Processing (NLP) has grown from an internet novelty to a reasonably robust tool.

While it hasn’t seen as much use in the corporate world as its cousin, Natural Language Generation (NLG), it has developed enough for enterprise use.

The most relevant NLP application right now is employing chatbots to provide 24/7 customer support availability. Customers can interact with a chatbot using normal, everyday language.

The sophistication of a bot varies widely. Some have very basic account support capabilities; others can guide a customer from selecting a product all the way through checkout.

At this stage of maturity users generally know they’re speaking to a chatbot, though NLP has evolved to the point where the bot doesn’t frustrate users by getting stuck or spitting out garbled answers.

Instead, bots provide a clear, straightforward path to resolving common customer issues. The convenience of having uninterrupted access to routine account services tends to negate any annoyance.

Virtual assistants also fall under the heading of NLP. These let users request analytics and services using natural language and receive replies either out loud or projected to a specific device.

There are virtual assistant integrations for a huge variety of popular enterprise programs. Some even provide a path for the assistants to complete purchases using pre-approved sites.

Looking forward

Some interesting trends are gaining traction right now. Connected “multi-cloud” strategies are maturing, and research firms like Gartner have been tracking the application of real-time analytics to automated insight generation.

For now, though, the analytics trends are the ones which have demonstrated their utility and staying power.

While nothing is guaranteed in the tech world, even tech-averse companies can expect at least a reasonable ROI from their adoption.

Are you interested in adding to your business analytics toolkit? Get a full assessment of your BI needs!

Request a Consultation

5 Ways to Build Internal Support for Your BI Initiative


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!

Request a Consultation

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


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:


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.


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.


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.


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.


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


  • 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.


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


  • 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.

Request a Consultation

The Easiest Way to Implement Business Intelligence For Enterprise


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.


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!

Request a Consultation

The Best Project Management Methodology for Business Intelligence


According to the Project Management Institute, Agile methodologies have become the gold standard for IT projects. 40% of organizations report using it most of the time. When companies just beginning to incorporate Agile are included that number jumps to 71%. These companies are responding to the sizeable increase in performance realized from Agile-guided projects, which are 28% more successful than those developed with more traditional methodologies.

The benefits of Agile aren’t limited to IT. Experts are starting to incorporate Agile ideology into other highly technical domains, most prominently business intelligence.

Agile methodologies have the potential to drive dynamic, responsive business intelligence processes.

What are the Core Values of Agile?

Agile sees a lot of use in software development, but its core principles have wide-ranging applicability. Consider the primary Agile characteristics:

  • Early, frequent delivery of usable products
  • Openness to change when doing so provides competitive advantage
  • Working solutions as a measure of progress
  • Sustainable development
  • Quality over quantity
  • Efficiency in planning and execution
  • Communication at all levels

At heart, Agile is about providing outstanding customer service and quality in as efficient and sustainable a manner as possible.

That has a lot of appeal for domains which can otherwise get bogged down in obscure details.

Business intelligence is one such field. It requires both technical skill and business savvy, and sometimes striking a balance between the two threatens to derail a promising project.

Challenges to Business Intelligence Projects

The term “business intelligence” covers a lot of ground.

It’s applied to a wide spectrum of techniques aimed at giving leaders the information they need to make logically sound, data-driven business decisions.

This includes finding inefficiencies and workarounds for them, cutting costs, increasing profit margins, and highlighting opportunities in time to act on them.

As might be expected from such an ambitious goal, business intelligence projects can have erratic success rates.

There are so many moving parts that one misstep potentially jeopardizes the entire project. Some of the most common reasons BI initiatives fail:

  • Data sources are insufficient or inaccurate
  • The final tool doesn’t meet the needs of users
  • Products take so long to create that they’re already outdated on release
  • Poor user experience ratings
  • Mismatched team schedules and technical philosophies

What’s the Best Methodology for Business Intelligence?

Taking Agile methodologies and incorporating them into BI projects makes it possible to mitigate or even avoid these problems altogether.

For instance, following the discovery process is a good way to create a comprehensive requirements list.

Gather all stakeholders together and find out what data they want or need on an ongoing basis. Look for overlapping needs as well as outliers.

Prioritize requirements as a group. Doing so makes the process transparent and reduces the risk of one category of stakeholders feeling minimized (which affects adoption rates).

Short, iterative sprints are excellent for breaking up obscure technical problems.  Emphasize creating something that can be used now and build on that.

Provide additional workable tools, processes, or data source at the end of each sprint. Think smaller in size but higher in quality for sprint scope.

Something that may work when stakeholders are skeptical: organize and prioritize projects by place in the business process to allow progress to build momentum.

As people see the value of Agile, they can more confidently embrace its methodologies.

One of Agile’s greatest strengths is regular feedback. Maintain an open channel of communication with those who will be using the project.

Welcome feedback and questions as a way to provide better data, and incorporate changes as they’re needed. Focus on user satisfaction as a measure of success.

Speaking of measuring success, the continual quality assurance process Agile recommends will keep business intelligence resources in top conditions.

Test systems throughout the development cycle to spot problems early, when they’re easiest to fix.

Schedule specific “source updating and validating” sprints at regular periods to eliminate the threat of using outdated data.

Aggressively seek out weaknesses to fix them as soon as possible. Tools that don’t work don’t get used, so proactive testing and repair protects the original investment.

Bending the Rules

A final word of caution: don’t get so dedicated to Agile that the business intelligence project suffers.

For example, many companies spend longer in the “discovery phase” than software developers might because business intelligence requirements tend to be highly complex.

Others have to go back over a testing phase to iron out a tricky component. That’s okay- if a certain part of the project needs more time, give it more time.

At the end of the day Agile is about results, not rules. Adopt the Agile concepts that offer an advantage and don’t stress over those that don’t apply.

There is more powerful business intelligence software on the market than ever, but all those incoming data streams can be overwhelming. Request a free consultation to find out how Concepta can organize all your business intelligence into an intuitive, customizable dashboard.


Request a Consultation

Going Digital on a Dime: Handling Business Intelligence on a Tight Budget


As artificial intelligence and automation mature, exciting new business intelligence tools are proving their worth.

It’s hard to deny the results early adopters have seen from those technologies.

For companies struggling to balance business intelligence with other digital transformation efforts, however, the expense of building BI software seems prohibitive.

They need an option that lets them “punch above their weight class” without destroying the IT budget.

The Trouble With Modern Business Intelligence

Using traditional methods, the cost of incorporating AI and automation into business intelligence presents an enormous barrier to entry.

Both require specialized software and dedicated storage space. Companies interested in modernizing their BI practices had to build their own system.

This involved a massive up-front investment in both time and money.

How massive? Even with the actual algorithms and analytical software licensed from a third-party vendor, companies had to:

  • Build on-site servers to store their data.
  • Commission or buy analytics and management software.
  • Hire a data science team to manage business intelligence.
  • Physically maintain and protect the servers.

The process takes a long time to show ROI, and once it does things get complicated.

To continue producing results it needs a steady influx of data.

That means bigger servers, more data scientists to interpret results, and rising peripheral costs (physical security, server maintenance, higher power requirements).

The expense made modern BI methods inaccessible for all but the largest global organizations.

Without those methods, though, businesses have trouble staying in the game.

By 2020 those who don’t effectively use their data will be losing $1.2 trillion to their better-informed competitors every year.

As CIOs realized the potential risks, demand rose for economical BI solutions.

Three game-changing strategies are helping companies regain their competitive edge.

Cloud Storage

The advent of cloud storage has done away with the need to build on-site servers.

Cloud databases function like renting a storage unit: monthly payments cover the actual storage space plus maintenance and physical security.

Because there’s less up-front investment, projects using cloud storage realize ROI in a shorter time frame.

Cloud storage has more than financial benefits. It’s fast and simple to set up. Scaling is as easy as upgrading a subscription.

The servers don’t need to be moved if the company’s physical location changes.

All things considered, cloud storage is hands-down the best option for responsive development.

Software As A Service (SaaS)

Building custom BI software is not always practical for companies without a clear vision of their BI needs.

For budget-conscious companies just exploring the high-tech BI space or for those with relatively routine needs, Software-as-a-service (SaaS) is a better solution.

SaaS can be loosely compared to cloud storage in that it’s a subscription service, but SaaS goes several steps further.

Instead of being simple storage, SaaS is BI software owned and maintained by a third-party vendor.

It’s accessed through the vendor’s site rather than being integrated into a company’s own systems.

SaaS software is usually designed for non-data engineers. That makes it intuitive to learn and use.

The pay-as-you-go subscription model is easy to budget for and has low start-up costs.

Some freemium options have no start-up costs, letting companies get a feel for how they’d use software before committing to premium features.

There are some drawbacks. Because SaaS software is designed for general use by non-technicians, its features may be limited or simplistic.

It’s hard to get exactly what is wanted in one product. Most companies end up using several different products to get the answers they need.

Data usually has to leave the company’s servers to be analyzed on the vendor cloud. That introduces a point of vulnerability for data breaches.

As security becomes a bigger priority, however, SaaS providers are creating solutions that better protect sensitive data.

Discussing security concerns with the vendor should alleviate most of this risk.

Consolidating Data and Analytics

One hidden cost of BI is labor. Especially when the budget is tight, employees are asked to do a lot of the data gathering and preparation manually.

This seems like a low cost solution – but it’s actually a very expensive way to do business intelligence.

When employees are asked to do their own data prep they lose as much as 80% of their workday to tedious, low-value tasks.

Their resulting data is pricier and less reliable than data prepared by software (which isn’t susceptible to human error).

Even when staff is given management tools, time is wasted correlating data from one system to another.

SaaS is easy on the budget, but it does mean using several “almost right” solutions rather than one custom system. 71% of companies admit to using 6 or more data sources while doing BI.

Commissioning a unified reporting dashboard built solves many self-service BI problems.

It costs significantly less than creating BI software from scratch, is easy to train on, and makes data more readily usable on a daily basis.

Plus, it can be added onto if another SaaS solution is added to the system.

Digital on a Dime

Business intelligence doesn’t have to be a tool limited to the Fortune 500.

Using these solutions, every company can spread their BI investment out over time and begin seeing results before traditional companies have finished building their servers.


How accessible is your BI data? Concepta builds intuitive, dynamic BI dashboards to put data in the hands of decision-makers. Set up your free consultation today!

Request a Consultation