Data Analytics - Arc Analytics

Why Your “AI Strategy” Might Be Missing the Foundation

by Nathan Mation | Oct 12, 2025 | AI, Data Services

Many teams feel the pressure to modernize reporting quickly. The result is a rush to buy tools, spin up dashboards, and promise smarter insights to leadership. What often happens next is disappointment. Reports do not match finance numbers, definitions shift from meeting to meeting, and trust erodes. The common thread is not the tool. It is the foundation beneath it. When the basics are weak, software only magnifies the gaps. The good news is that AI Strategy is achievable with a clear plan and steady ownership.

The Rush to Modern Reporting and Why It Backfires

There is a real sense of urgency across industries to upgrade reporting. Competitors show off slick visuals. Vendors share compelling demos. Leadership sets ambitious timelines. In that environment, it is easy to believe the next platform will fix long-standing issues. What follows is predictable. The new system connects to the same messy sources. The same conflicting definitions move forward untouched. Data quality problems resurface in new dashboards. Instead of better answers, teams now have faster confusion. Progress depends less on buying something new and more on preparing what you already have.

The Three Pillars Most Teams Skip of AI Strategy

Strong reporting sits on three simple pillars. They are not glamorous, but they are non-negotiable.

Pillar 1: Clean and Centralized Data

Data that lives in many places produces different answers to the same question. Customer records exist in CRM, billing, and support. Product names differ across catalogs. Dates are stored in different formats. A sales total in one system does not match the finance ledger in another. When reports draw from these sources directly, accuracy becomes a guessing game. A better approach starts with a data audit. Identify key systems. Map where core fields live. Profile the most important tables for completeness and duplicates. From there, consolidate into a single source of truth. That can be a data warehouse, a data lakehouse, or a well-structured dataset in a governed platform. The format matters less than the principle. Put the most important data in one place, clean it, and keep it in sync. When teams pull from the same foundation, discrepancies drop and trust rises.

Learn more: Data Integration Services

Pillar 2: Clear Business Logic and Definitions

Numbers do not explain themselves. Someone has to decide what counts as active users, what qualifies as revenue, and when a deal is considered closed. Without shared definitions, every department tells a slightly different story. Sales reports bookings, finance reports revenue recognition, and operations reports shipped units. None are wrong, but without alignment,dxsc they do not add up in the same meeting. The fix is straightforward. Write down the definitions that matter most. Document how each metric is calculated. Note inclusions, exclusions, time frames, and edge cases. Put these rules in a data dictionary that everyone can access. Then, implement the logic consistently in your data pipelines and models. When a metric changes, update the documentation and notify stakeholders. Clear definitions are the language of your business. If you want clear answers, you need a shared vocabulary.

Learn more: Business Intelligence Consulting

Pillar 3: Governance and Ownership

Quality does not sustain itself. Someone must own it. In many organizations, data issues float between teams. Security is owned by IT, definitions are owned by analysts, and access is managed ad hoc. Over time, small exceptions become fragile patterns. A simple governance framework solves this. Assign data owners for key domains like customers, products, and finance. Define who approves changes to definitions and who grants access. Set up basic controls like role-based permissions and review logs. Schedule regular checks on data quality and pipeline health. Good governance is not bureaucracy. It is clear about who makes which decision and how changes move from idea to production. With ownership in place, teams stop firefighting and start improving.

Learn more: Data Integration Services

What AI Strategy Actually Needs to Succeed

Successful reporting follows a reliable sequence. First, assess your current state. List the systems, map the flows, and highlight the top pain points. Second, clean and centralize the most important data sets. Third, standardize definitions and encode them in your models. Fourth, automate the refresh process so data arrives on time without manual effort. Finally, add advanced features like predictive insights or natural language queries once the foundation is steady. This order matters. When you reverse it, you spend more time reconciling than learning. When you follow it, you create steady momentum and measurable wins.

Foundation Checklist: What to Verify Before You Build AI Strategy

The table below turns the foundation into clear checkpoints. Use it to structure your assessment and plan.

Area	What good looks like	How to verify	Common gaps
Sources and lineage	All key systems listed with data flows mapped	Role-based access with review process	Shadow exports and undocumented pipelines
Data quality	Key tables have high completeness and low duplicates	Profiling reports and data tests	Missing keys and inconsistent formats
Centralization	One trusted store for core data sets	Warehouse or governed dataset in use	Direct reporting against many sources
Definitions	Top metrics documented with clear logic	Data dictionary accessible to all	Multiple versions of the same metric
Access and security	One-off access and stale accounts	Permissions matrix and audit trail	One off access and stale accounts
Refresh and reliability	Automated schedules with monitoring	Pipeline run logs and alerts	Manual refreshes and silent failures

Quick Wins vs Long Term Improvements

It helps to separate immediate fixes from structural change. Quick wins often include standardizing a handful of high-visibility metrics, publishing a single source sales or revenue dataset, and automating a daily refresh for a key dashboard. These steps improve confidence fast. Long-term improvements include consolidating duplicate systems, establishing a formal data governance council, and investing in a documentation culture. Both tracks matter. Quick wins build trust. Structural work sustains it.

How Arc Analytics Builds the Foundation, Then Adds the Advanced Layer

Our approach starts with an assessment. We inventory your systems, map data flows, and identify the top five gaps that block reliable reporting. Next, we centralize and clean the most important data sets. We work with platforms like Qlik Cloud and Snowflake when they fit your stack, and we implement models that reflect your business rules. We help you document definitions in plain language and apply them consistently. We set up simple governance that names owners and clarifies decisions. Only then do we add advanced features on top. The result is not only better dashboards but also a foundation that scales as your questions evolve.

Explore our services: Data Strategy Consulting | Qlik Cloud Services | Staffing for Data Teams

A simple view of our approach is shown below.

Phase	Objective	Typical outputs
Assess	Clean and centralized	System inventory, data flow map, gap list
Clean and centralize	Create a trusted core data set	Warehouse tables, profiling results, tests
Standardize	Align business logic and definitions	Data dictionary, modeled metrics, change log
Automate	Ensure timely, reliable updates	Scheduled pipelines, monitoring, alerts
Enhance	Add predictive and natural language features	Advanced reports and guided insights

Your Next Step: The Foundation Assessment

If you want to know where you stand, start with a short assessment. In thirty minutes, we can review your current setup, highlight the top risks, and suggest a clear next step. You will receive a readiness score, a concise gap analysis, and a simple plan to move forward. If you already know your top pain point, we can focus there first. If you prefer a broader view, we can cover the end-to-end picture.

Ready to get started? Schedule your free foundation assessment today or reach out to our team at support@arcanalytics.us.

Build the Foundation First

Modern reporting delivers real value when it sits on a steady base. Clean and centralized data reduces noise. Clear definitions remove debate. Governance and ownership keep quality from drifting over time. With these pieces in place, advanced features become helpful rather than distracting. The path is practical and within reach. Start with an honest look at your current state, take a few decisive steps, and build momentum from there. If you want a partner to help you do it right, we are ready to assist.

Take action now: Contact Arc Analytics to assess your reporting foundation and build a plan that works.

Signs Your Organization Needs a Data Consultant Now

by Nathan Mation | Jul 30, 2025 | Data Services, Qlik Services

When Is It Time to Call a Data Consultant?

Every organization wants to turn data into a competitive edge, but for many in healthcare, education, state government, and small to medium-sized businesses (SMBs), the path isn’t always clear – you’re partnered with a strong data consultant. If you’re struggling to connect the dots between your data and real-world results, you’re not alone. Recognizing the signs early—and knowing where to get help—can make all the difference.

If you’re in healthcare, you might notice that patient data is scattered across EHRs, billing, and departmental systems, making it tough to see the full picture. In education, student information, learning management, and alumni data often live in silos, blocking a unified view of student progress. State governments face similar hurdles with legacy systems and fragmented agency data. SMBs, meanwhile, often collect sales and marketing data but lack the resources to turn it into actionable insights.

Don’t let data challenges hold you back. Explore our data analytics consulting services or contact us to see how a data consultant can help.

Industry-Specific Data Challenges that a Data Consultant Solves

Healthcare

Healthcare organizations often struggle to translate raw data into better patient care. Disconnected systems, inconsistent records, and a lack of clear data strategy can slow progress. If your IT team is overwhelmed or dashboards aren’t delivering the insights you need, it’s time to consider outside expertise. Learn more about our healthcare data analytics services.

Education

Educational institutions face challenges connecting student data to learning outcomes. Siloed systems, unreliable attendance or grading data, and no clear roadmap for data-driven teaching can hinder student success. If your team can’t keep up or you’re not spotting at-risk students early, a data consultant can help. See our education analytics solutions.

State Government

State agencies often find that citizen data isn’t driving better services due to fragmented legacy systems and inconsistent reporting. Without a unified data strategy, it’s hard to inform policy or improve programs. If your IT team is stretched thin or your reports aren’t actionable, it’s time to act. Discover our government analytics expertise.

Small & Medium Businesses (SMBs)

SMBs may collect plenty of data but struggle to make sense of it. Data scattered across apps, duplicate or missing customer info, and limited IT resources can make it hard to compete. If you can’t see sales trends or predict customer needs, you’re missing out on growth opportunities. Check out our data analytics services for SMBs.

Common Data Challenges: At a Glance

Challenge	Healthcare	Education	State Gov	SMBs
Data silos & fragmentation	✔	✔	✔	✔
Poor data quality	✔	✔	✔	✔
No clear data strategy	✔	✔	✔	✔
Overwhelmed IT teams	✔	✔	✔	✔
Weak data visualization/reporting	✔	✔	✔	✔
Not using advanced analytics	✔	✔	✔	✔

What a Data Consultant Brings

A data consultant offers a fresh perspective, specialized industry knowledge, and the technical skills to solve your toughest data problems. They can help you break down silos, improve data quality, and build a roadmap for success. Plus, they empower your team with best practices and keep you up to date with the latest tools and trends.

Ready to take the next step? Meet our team or explore our full range of services.

Conclusion: Invest in Data-Driven Success

Don’t let data challenges slow you down. Whether you’re in healthcare, education, government, or running a growing business, the right consultant can help you unlock the full potential of your data. Contact us today to start your journey toward smarter decisions and better results.

Adding Tabler Icons to Qlik Dashboards

by Austin Spivey | Jul 15, 2025 | Learning

Qlik doesn’t really allow for icons. When creating dashboards in Qlik Cloud, it can be very helpful to add icons to spruce up KPIs, titles, and tables. There are hundreds of use cases for adding some visual flair using icons, but it can be cumbersome to add icons to objects in Qlik because there are very few built-in icon options.

So, how can we go about adding some icons to our dashboards in an easy and expressive way?

We can use a font! But wait, we’re talking about icons, not text. So how will a font help us?
It turns out that fonts can pack in far more than just standard characters like letters, numbers, and punctuation. One example of a “supercharged” font is Tabler Icons.

Tabler Icons is an open source project that bundles thousands of beautiful icons into multiple formats that you can use freely in your web projects. One such format is a webfont, specifically .tff which is a TrueType font type.

How can we use this font in Qlik?

We’ll add it to a custom Qlik theme and choose icons in our dashboard using variable expansion with a parameter.

Don’t worry if this doesn’t quite make sense yet! Let’s go through each step now.

Steps to set up

Download Tabler Icon webfont.

We can find the tabler-icons.ttf font file in the Tabler Icons Webfont package on the NPM website:

We can download the font file directly from here: https://cdn.jsdelivr.net/npm/@tabler/icons-webfont/dist/fonts/tabler-icons.ttf

Create a new or open an existing Qlik theme.

If you don’t already have a custom theme to add this font to, go ahead and create one based on the instructions laid out on the Qlik Help website. You can also look online for a Qlik theme generator to help get you started.

Add tabler-icons.ttf to the Qlik theme folder.

Move the tabler-icons.ttf file to your custom Qlik theme folder. It should look similar to this:

Add @font-face to the theme CSS file.

Open your theme’s .css file and add this snippet at the top:

@font-face {
  font-family: "Tabler Icons";
  src: url("tabler-icons.ttf") format("truetype");
  font-weight: normal;
  font-style: normal;
}

Save and close the file.

Add or modify the fontFamily property in the theme JSON file:

Open your theme’s .json file and add this snippet near the top:
```
"fontFamily": "Open Sans, Tabler Icons, sans-serif"
```
Here’s an example of what it should look like:

ℹ️ Note that in our screenshot above, my snippet includes the Open Sans font, as I want for that to be the primary font for normal characters like letters and numbers. You can replace that with any of the default Qlik Cloud font options:

Upload file to Qlik Cloud or Qlik Sense Client-Managed.

To add your custom theme to Qlik, you must first save the theme folder as a ZIP file.
How to upload your theme to Qlik Cloud
- You can follow this Qlik Help page guide on how to upload your theme ZIP file to Qlik Cloud.
- After uploading the theme ZIP file, you should see the theme in the Admin Console:

How to upload your theme to Client-Managed
- You can follow this Qlik Help page guide on how to upload your theme ZIP file to Qlik Sense Client-Managed.
Open a Qlik app and add this Qlik script to the Data Load Editor in the Main section:
Set GetTablerIcon = Chr(Num#(‘$1’, ‘(hex)’));

Your Main script should look like this:

How to use icons in a dashboard

Go to https://tabler.io/icons and search for the icon you want.
Search for “bar chart” and click on the first icon in the results:

In the box that appears, click on the hex value to copy it:

Go to the app Sheet view and switch the app theme to use our uploaded theme.

In your Qlik app, select any Qlik object, and then choose an expression property.

For example, you can create or select a bar chart object and then open the expression editor for the Title expression.

In the property’s expression editor, we’ll use dollar-sign expansion with our GetTablerIcon variable and use the our copied Tabler Icon hex code as the parameter.

Make this the expression:

=$(GetTablerIcon(ea59)) & ' Sales by County'

– Then select the Apply button to save that.

You should now see your icon in the chart title!

If your icon doesn’t appear or you see a placeholder character in the chart title where our icon should be, you probably just need to update the font property.

To do this, go to the chart styling tab:

Find the font property we want to change (Title in this example) and then choose the option that includes Tabler Icons:

ℹ️ Note that if you want to “pair” the Tabler Icons font with a primary font that regular characters will use, refer back to step 5.

Summary

You should now be able to use Tabler Icons anywhere in a Qlik dashboard that supports text expressions and changing the font!

That should get you very far.Try changing the font color and size to see how the icons scale very well and can be recolored just like text.

Building A Robust Data-Driven Culture

by Nathan Mation | Jul 9, 2025 | Data Integration, Data Services, Data Strategy

In today’s fiercely competitive business landscape, data has moved beyond the realm of simple record-keeping to become the very engine of strategic advantage. Organizations that can effectively harness the insights hidden within their data streams are demonstrably more agile, innovative, and ultimately, more successful. However, the journey towards becoming a truly data-driven organization is not merely about deploying sophisticated analytics platforms. It requires a fundamental shift in culture, a deep-seated commitment that permeates every level of the organization, from the executive suite to individual contributors. This comprehensive guide will navigate the essential steps involved in cultivating a robust data-driven culture, underscoring its profound benefits and illuminating the critical role of people, processes, and technology in this transformative endeavor.

Laying the Foundation: Identifying Key Pain Points and Opportunities

The initial and foundational stage in building a data-driven culture involves a collaborative and thorough effort to pinpoint the specific areas within the organization where data can exert the most significant positive influence. This process extends beyond simply identifying obvious operational bottlenecks or areas of inefficiency. It necessitates engaging stakeholders from across all departments – sales, marketing, operations, finance, customer service, and beyond – to understand their unique challenges and the questions they struggle to answer with existing information. For instance, the marketing team might grapple with understanding which campaigns yield the highest return on investment, while the sales team might lack clarity on the characteristics of their most successful leads. Operations could be struggling with unpredictable supply chain disruptions, and customer service might be reactive rather than proactively addressing potential issues.

Furthermore, the focus should not solely be on rectifying problems. A truly data-driven mindset actively seeks opportunities where data can fuel innovation, enhance the customer experience in meaningful ways through personalization, optimize the allocation of resources across various initiatives, and even identify entirely new business models. By involving a diverse range of perspectives, organizations can uncover a broader spectrum of both pain points ripe for data-driven solutions and untapped opportunities waiting to be unlocked. Prioritizing these identified areas based on their potential impact on key business objectives and the practical feasibility of implementing data-driven solutions will ensure that initial efforts are strategically aligned and deliver tangible value, fostering early buy-in and demonstrating the power of a data-centric approach.

Empowering Solutions: Leveraging Data to Solve Problems and Drive Innovation

Once the key pain points and promising opportunities have been identified, the next crucial step involves strategically applying various methodologies of data analysis to extract meaningful insights and drive tangible improvements. This encompasses a spectrum of analytical techniques, each suited to answering different types of questions. Descriptive analysis provides a historical overview of what has occurred, offering valuable context. Diagnostic analysis delves deeper, seeking to understand the underlying reasons and correlations behind observed trends. Predictive analysis leverages historical data and statistical modeling to forecast future outcomes and anticipate potential challenges or opportunities. Finally, prescriptive analysis goes beyond prediction by recommending specific actions and interventions to achieve desired results.

For example, if a sales team is struggling with high customer churn, diagnostic analysis might reveal specific customer segments or interaction patterns that are strong indicators of attrition. Predictive modeling could then forecast which current customers are most likely to churn, allowing for proactive intervention. Prescriptive analytics could even recommend targeted strategies, such as personalized offers or enhanced support, to mitigate this risk. Similarly, in product development, analyzing customer feedback data (both structured and unstructured) can provide invaluable insights into unmet needs, guiding the creation of innovative new features or products. The process of leveraging data for problem-solving and innovation is iterative, requiring a willingness to formulate hypotheses, rigorously test them against available data, and refine analytical approaches based on the evidence uncovered. Embracing a culture of experimentation, including A/B testing different data-driven strategies, is essential for validating their effectiveness and fostering a continuous cycle of improvement and learning.

Cultivating Data Fluency: The Cornerstone of a Data-Driven Culture

The successful and sustainable embedding of a data-driven culture within an organization fundamentally relies on cultivating a high degree of data fluency across all levels of its workforce. This does not imply that every employee needs to become a data scientist or possess advanced statistical expertise. Instead, it signifies fostering a widespread comfort level in working with data, enabling individuals to understand basic data concepts, interpret visualizations, formulate relevant questions based on data, and confidently utilize data-backed insights in their daily decision-making processes. The specific levels of data literacy required will naturally vary depending on individual roles and responsibilities. However, a foundational understanding of data privacy, ethical data usage, and the ability to critically evaluate data sources are essential for everyone.

Organizations can adopt a multi-pronged approach to elevate data literacy. This includes implementing comprehensive training programs tailored to different skill levels and roles, creating easily accessible internal resources such as data glossaries, style guides for data interpretation, and case studies showcasing successful data application. Mentorship programs that pair data experts with colleagues seeking to enhance their skills can also be highly effective. A critical element is ensuring that data is presented in an accessible and understandable manner for non-technical users, often through user-friendly dashboards and intuitive data visualization tools that abstract away unnecessary complexity. Leadership plays a pivotal role in championing data literacy initiatives by actively demonstrating the value of data in their own decision-making processes, visibly supporting training efforts, and fostering an environment where asking data-related questions is not only encouraged but expected. Ultimately, nurturing a culture of intellectual curiosity, where employees are empowered to explore data and seek evidence-based answers, will solidify data fluency as a core organizational competency and drive widespread adoption of data-driven practices.

Equipping Your Team: Choosing and Implementing the Right Data Tools

The strategic selection and effective implementation of appropriate data tools are critical enablers of a data-driven culture. The right tools can democratize access to data, empower users to perform their own analyses, and streamline the process of generating insights. When evaluating potential data tools and platforms, organizations should consider several key criteria. Usability for a diverse range of users, regardless of their technical proficiency, is paramount. Seamless integration capabilities with existing systems and data sources are essential to break down silos and ensure data accessibility. Scalability to handle growing data volumes and evolving analytical needs is crucial for long-term viability. Robust security features are non-negotiable to protect sensitive data and ensure compliance with relevant regulations. Finally, the overall cost-effectiveness of the tools, considering both initial investment and ongoing maintenance, must be carefully evaluated.

Platforms like Qlik Cloud offer a powerful and versatile suite of capabilities designed to foster a data-driven environment. Their intuitive and interactive data visualization tools empower users to create insightful dashboards and reports with minimal technical expertise, while their robust data integration features facilitate the connection and harmonization of data from disparate sources. Features such as collaborative analytics enable teams to work together on data exploration and insight generation, and embedded analytics capabilities allow for the seamless integration of data insights into existing applications and workflows. However, simply selecting the right tools is only part of the equation. Successful adoption necessitates a well-planned implementation strategy, comprehensive training programs to ensure users can effectively leverage the tools’ features, and ongoing support to address any technical challenges or user questions. Furthermore, establishing clear data governance policies and procedures is essential to ensure the quality, accuracy, and trustworthiness of the data being utilized within these tools, fostering confidence and driving adoption.

Conclusion: Embracing Data as the Engine of Success

In conclusion, the journey towards building a truly robust and impactful data-driven culture requires a holistic and sustained effort that encompasses people, processes, and technology. By systematically identifying key pain points and opportunities, empowering data-driven solutions, cultivating widespread data fluency across the organization, strategically selecting and implementing the right data tools, and diligently sustaining the momentum through continuous learning and leadership commitment, organizations can transform data from a latent asset into the very engine of their success, driving innovation, enhancing efficiency, fostering deeper customer understanding, and ultimately achieving a significant and sustainable competitive advantage in today’s data-rich world.

What to Expect From a Data Analytics Consulting Partner

by Nathan Mation | Jun 30, 2025 | Learning

Navigating the world of data analytics can feel like trying to decipher an ancient language. You know the potential is there – those hidden insights that can propel your business forward – but unlocking them often requires a skilled guide. That’s where a data analytics consulting partner comes in. But just like choosing the right travel companion, finding the right partner can make all the difference between a smooth journey and a frustrating detour. This isn’t just about someone setting up a dashboard and calling it a day. A true partner becomes an extension of your team, deeply understanding your unique challenges and working collaboratively to achieve your specific goals. So, what should you really expect from this kind of relationship?

What Should You Be Receiving? It’s More Than Just Deliverables.

When you engage a data analytics consulting partner, you’re not just buying a service; you’re investing in expertise and a collaborative relationship. Here are some key things you should expect to receive:

Flexibility That Fits Your Needs: Forget rigid contracts and pre-packaged solutions. A good partner understands that your needs can evolve. Expect pre-authorized hours that provide budget control while allowing for necessary work to be completed. Think of it as setting a clear boundary, like saying, “Let’s scope this project within 40 hours, and if we need more, we’ll talk.” This demonstrates respect for your budget and ensures transparency. Furthermore, look for flexible service level agreements (SLAs). These shouldn’t be one-size-fits-all. A partner should be willing to tailor SLAs – perhaps a standard four-hour response time for typical requests, which might even adjust to a quicker 30-minute response during critical periods – all tied to clearly defined scopes of work and agreed-upon hourly rates. This adaptability shows they’re truly invested in supporting your business rhythm.
A Consistent and Dedicated Point of Contact: Imagine having to explain your project to a new person every time you reach out. Frustrating, right? Expect a dedicated, 1:1 relationship where you work with a consistent team that builds a deep understanding of your business, your data, and your Qlik Cloud environment. This eliminates the inefficiencies of multiple touchpoints and the impersonal feel of large, impersonal firms relying on offshore subcontractors who may not have the same level of direct investment in your success. You deserve a team that’s in the trenches with you, not just filling out timesheets from afar.
Proactive Partnership, Not Just Order-Taking: A great consulting partner doesn’t just wait for you to tell them what to do. They should be proactive, bringing insights and suggestions to the table based on their understanding of your business and the capabilities of Qlik Cloud. Expect regular check-ins – not just status updates, but strategic conversations about progress, potential roadblocks, and future opportunities. They should be genuinely invested in understanding your specific business goals and tailoring their approach to help you achieve them.

Signs You Might Need a New Qlik Consulting Partner: Don’t Settle for Less.

Are you getting everything you should be from your current data analytics partner? Here are some red flags that might indicate it’s time for a change:

Silence is Not Golden: A lack of proactive communication or consistently missed deadlines are clear indicators that your partner isn’t prioritizing your needs. You shouldn’t have to constantly chase them for updates or feel like your project is on the back burner.
Quote Chaos: Receiving inaccurate quotes or having to constantly request updated pricing signals a lack of attention to detail and can lead to budget surprises. Transparency in pricing is crucial for building trust.
The Price Doesn’t Feel Right: Be wary of price gouging, an unfortunately common practice in technology sales, especially within sectors like state and local government. A trustworthy partner will be upfront and transparent about their pricing and licensing models, ensuring you’re paying a fair market value for the Qlik Cloud products and services. They should be working to get you the best value, not just maximizing their profit at your expense.
Where’s the Innovation?: If your partner isn’t bringing innovative solutions or demonstrating a deep understanding of the latest Qlik Cloud features and how they can benefit your specific industry, you might be missing out. A good partner stays ahead of the curve and helps you do the same.
They Don’t “Get” Your Business: A partner who doesn’t take the time to understand the unique nuances and challenges of your specific industry is less likely to deliver truly impactful solutions. Generic advice won’t cut it.
The Feeling’s Not Mutual: Ultimately, if you feel like your current partner isn’t truly invested in your success, isn’t communicative, or isn’t providing the level of service you expect, it’s a strong sign that it might be time to explore other options.

The Value of Industry Expertise: Why It Matters.

While a broad understanding of data analytics is essential, a partner with proven experience in your specific industry can bring invaluable insights. For example, Arc analytics specializes in education, healthcare, and government sectors, where we’ve developed a deep understanding of the unique data challenges and regulatory landscapes. However, our experience isn’t limited to these verticals. Our history of completing hundreds of projects across several sectors demonstrates our adaptability and ability to apply our Qlik Cloud expertise to diverse business needs. This cross-industry experience allows us to bring best practices and innovative solutions from different fields to your specific challenges.s

Deliverables: Tailored Solutions Designed for You.

Forget the idea of a one-size-fits-all solution. A quality data analytics consulting partner understands that your needs are unique. Expect solutions that are configured and customized to the specific scope of work you define. This means the dashboards, reports, and integrations you receive are designed to answer your specific business questions and track your key performance indicators. While the underlying technology might be consistent, the final deliverables should feel like they were built for you, not just for anyone. We also believe in transparent reporting when it comes to accounting, so you have a clear understanding of the investment you’re making.

Finding the Right Fit: It’s About More Than Just Technology.

Choosing a data analytics consulting partner is a significant decision. It’s about finding a team that not only possesses the technical expertise with Qlik Cloud but also prioritizes clear communication, genuine collaboration, and a deep understanding of your business. You deserve a partner who feels like a natural extension of your own team, dedicated to helping you unlock the full potential of your data. Ready to explore how a dedicated and experienced Qlik Cloud partner can help you achieve your data analytics goals? We invite you to reach out and discover the difference a true partnership can make. Contact our team.