Your marketing team just launched a campaign based on last quarter's outdated customer data. Meanwhile, your sales team is actively pursuing leads that customer service flagged as churned two weeks ago. Your finance team spent eight hours yesterday manually reconciling numbers that don't match across three different systems.

Sound familiar? It's not just inefficiency—it's the sound of dollars walking out the door every day. And it's completely preventable.

Having established that strategic businesses collect signals from across their operations, the next step for most companies is to start identifying patterns and set about interpreting what story the data is telling them. This process can be referred to as analysis, evaluation, interpretation, or synthesis; regardless of the term a company chooses, the goal is the same: to understand the data. The quality of a business's interpretive process is a key indicator of its future performance. In numerous case studies, companies that invest in evaluating their data have achieved up to a 300% ROI, a significant increase in profits, and reduced reporting time from days to minutes. Data collected from all corners of an operation can be leveraged to forecast, strategise, optimise and scale. A company must possess both the discipline and the competency to accurately interpret its vital signs.

Evaluating data is a significant roadblock for many businesses. Data synthesis can be intimidating, so let's break down some of the key concepts.

Here's what we'll look at together:

1. Methods for evaluating data
2. How data is stored and accessed once it's collected
3. The common issues that affect synthesis
4. Why do some businesses choose to ignore their data?
5. Practical steps any business can take to interpret data with confidence

Methods for Evaluating Data

There are several ways a business can evaluate data, but most are a variant of these four common approaches.

1. ETL (Extract, Transform, Load): Data is taken from its source (think analytics tool, sales engine, spreadsheet, or monitoring system) and then formatted for evaluation. Numeric data, code data, CSV data, and sales data can all be formatted differently upon collection. They therefore need to be transformed to facilitate synthesis, especially when a high volume of data is streaming in. Finally, the formatted data is loaded into a final endpoint, such as a 'data warehouse', where it is backed up, secured, and then accessed for analysis. You might be thinking, "What is the difference between a data warehouse and a database"? The difference lies in the currency of the data; databases store current data, while data warehouses generally store historical data. Databases are designed for short-term access, while data warehouses are intended for long-term storage. A business that uses the ETL method may do so because it lacks the in-house competency or computing power to analyse the data. In that case, they may tidy the data and hand it to a third-party business for analysis and reporting. Understanding the difference will help you select the right solution.
2. ELT (Extract, Load, Transform): When a business has strong computing power at its disposal, it may choose to load extracted data in its current form, allowing its internal systems to then transform and report on the data. This method is popular but also expensive, as it requires the computing power needed to evaluate massive amounts of data.
3. Real-Time Integration: Businesses can stream data securely from its source in real-time to multiple destinations. Businesses can use 'change data capture' systems to watch for changes in data and report those changes quickly. Ideal for companies that require low-latency feedback from their operations, this method can be highly customisable, but it can also attract a premium cost.
4. API-Based Integration: Application Programming Interfaces (APIs) enable the real-time extraction of data from databases, data warehouses, and data clouds, allowing for the implementation of changes based on live data. API integrations can introduce the capability for data evaluation for much smaller businesses, as they are often easier to access with tools like Zapier or Make.

Businesses choose a method based on the complexity of their data, the volume of the data, and their in-house competency and computing power. Regardless of their position in the market or investment level, any business can consider a method to evaluate its vital signs and adjust accordingly, because data is now more accessible than ever. We've already touched on databases, data warehouses, and data clouds, all of which serve as storage containers for various types of data. Without delving too deeply into IT jargon, there are other, more nuanced versions of these storage solutions, such as data lakes, data fabrics, and data mesh, that a business may leverage to their benefit.

The next set of challenges arises from how business access their data internally.

The Problem of Fragmented Data

Two-thirds of enterprise businesses have flagged one crucial issue with data evaluation as the primary pitfall to successfully leveraging their data: data silos. A data silo occurs when data is collected and stored, but is not accessible to those who need it, when they need it, and in the format they require. Consider how frustrating it would be to know that your business has a large shed full of potentially helpful information, but you can't find the address, the stock manifest or the key to the front door. If this scenario were to play out in the digital world, this is what the effect of a data silo would look like. In an enterprise business, this can occur with thousands of silos, tucked away neatly but rendered ineffective by protocol mismanagement, lack of access, and diminished visibility.

When businesses silo data, it leads to a variety of issues, including:

1. Incoherent decision-making: when various teams have only some of the information they need to operate promptly, their rationale is only half-informed. This leads to waste, higher levels of risk, loss of revenue and internal discord. Data siloes make it very hard to obtain a clear view of how things truly are. While a data silo may be a digital problem, it can have an outsize impact on very tangible factors in the real world.
2. Inconsistent reporting: Data siloes introduce multiple competing versions of the truth, which slows decision-making, erodes confidence in both the data and the team, and makes unity in operations difficult to manage. For example, if the data in one silo tells a story of progress while another contains a warning signal, evaluation is impossible when the whole team can't see both and hold them in tension.
3. Lapses in security: When data is fragmented, security often suffers. Large-scale data breaches have become far too common, even for enterprise-level organisations. Behind the scenes, two-thirds of businesses with data silos report experiencing data breaches, indicating that this issue is far-reaching and pervasive.
4. Decreased collaboration: Teams that have siloed data find it harder to work together. The majority of businesses that have siloes in their operation have a high proportion of staff reporting that they feel bottlenecked.

The most important thing a business can do in this space is ensure that it has practical, manageable, and clearly articulated methods for accessing, storing, and sharing data. Their data protocol must provide prompt access to approved parties while maintaining security at every juncture.

In addition to tackling data silos, businesses also face the challenge of estimating the quality of the data they've collected; whether it is reliable, actionable, and can be trusted. If data is the raw material, the quality of the data determines the quality of the house. Poor-quality data is both costly to the bottom line and prohibitive to progress. When considering issues of security, integration, complexity, governance, competency, and compatibility, combined with the price tag of software designed to help, many businesses opt to do nothing at all, whether by default or by choice.

Another critical roadblock to investment in data evaluation hinges on the philosophy of the business leaders. Resistance to change, scepticism of new methods, and sluggish hiring of skills, all under the guise of cost elimination, are the usual suspects that keep a business locked into outdated data evaluation systems or, even worse, apathy. While the costs of implementing change are not negligible, the downsides regularly far outweigh the investment needed to improve. The most crucial step is the first, so let's get practical about what a business can do to get started.

Practical Steps to Effective Data Synthesis

When preparing to enact change, the most important thing is to:

Set a Clear, Measurable Goal‍

There's more than enough literature available about how to set a SMART goal, but the premise is simple: the only way to know that your team has scored is to have a determined goal post. Here are some questions to help you begin to consider what that goal-post might be for your business:

• What questions are we constantly thinking about that we'd love to have answered definitively about our business?
• How can we tell when our business is growing in the right direction?
• What data matters most to where we are right now as a business?
• What would give us the confidence to innovate or expand the business?
• What would a 'check-engine' light look like for us as a team?

Audit Your Data Sources and Storage‍

Next, take a detailed inventory of where your data signals originate, and more importantly, where they ultimately end up. Be meticulous and reductive: consider every source and look for ways to consolidate your storage. Identify potential weaknesses in your security and explore methods to minimise the costs of storing your data without compromising access. Probe the following aspects:

• Do we need five 'clouds' or could one unified cloud do the job?
• Where are we over-spending on technology that we could strategically reallocate?
• How fragmented is our data?
• Do we have data-silos that need demolishing?
• Is our data easily accessible and shareable?
• Do our data tools have pathways for integration, such as APIs?

Delegate Access and Authority‍

Ensure that the right people (and only the right people) have access to your crucial data and the clearance to share it with others. Your data is vital to your success; to see it leak into the hands of a competitor (or worse) could spell disaster. Consider:

• What level of access does your leadership team need?
• What level of access do various team members need?
• Who is the final decision-maker on access authority?
• Does your business have a data-security policy?
• What is your response plan in the event of a breach?

Select and Implement a Method‍

Earlier, we mentioned a few avenues businesses use to synthesise their data into actionable insights. Consider the skills and capital you have, then select and shape an approach while pondering the following questions:

• How much data are we collecting and storing?
• How often do we need to prepare and report on that data?
• Who on our team is competent to oversee that process?
• Is our data of sufficient quality?
• How are we evaluating the quality of our data?

Identify and Leverage Integrations‍

Once you've invested in a method and established a framework, bring your data together in a meaningful way by utilising integrations. Bringing your data together into one place makes the evaluation process more granular, insightful, and enjoyable. Consider the following:

• What data tools do we have that can be integrated?
• How often do we want that data to be updated?
• What data would we as a team most benefit from easy access to?
• What time-sensitive information would help us move strategically?
• What factors prevent us from making decisions in a timely way, and might access to data remedy this?

Wrapping Up

We've covered much ground, but we're confident that you've learned the following if you've read this far:

• The benefits of evaluating your data and the consequences of neglecting synthesis
• The four primary methods businesses use
• The way that digital data is stored
• The danger of data silos
• Some of the reasons why businesses don't invest in synthesis
• Practical steps to act smart and evaluate data in your business

As we wrap up, ask yourself the following question: If my competitors were to examine my data evaluation process, would they be inspired to chase after me, or put their feet up? Where your business finishes on the scoreboard is determined by the effort you put into this crucial aspect of running a business in a technology-driven world.

Following this series of steps is a great starting point for any business looking to extract more value from its data while avoiding the common setbacks, pitfalls, and frustrations associated with digital transformation. Our free AI Dashboard Resource is another valuable tool we've designed to help businesses gain momentum. Our team at Via is ready to assist companies with our tried and proven process. If any of the topics we've addressed in this article have resonated with you, let's continue the conversation.