Killing Data Silos: The Ultimate Guide to data operations

by Riversafe

Killing Data Silos: The Ultimate Guide to data operations

An Introduction to Data Silos

What is a data silo?

A data silos describes a situation where a certain source of data can only be accessed by particular groups. This can occur because of technology issues, team communication issues, or wider business structure issues.

What problems do they cause?

It may not seem like a big deal, but ultimately data silos cause a plethora of resource and productivity implications which can have subsequent impacts on business performance.

For example, if teams can’t access the files they need in a straightforward way, they can be delayed on a project by several hours or days, their customer then receives work late, and then the company loses a valuable client. This is a very specific example, but data silos are generally a smaller sign of bigger problems; indicating that the internal structure or mind set of a business is disconnected and inefficient.

What is effective DataOps?

DataOps centres around 3 primary concepts; people, data, and integration. The aim is to eliminate silos and encourage connected working environments.

Good data operations will see teams and departments working cohesively together, data and workflows being shared, and technology seamlessly integrated throughout a business. This kind of interconnected environment enables more streamlined activity and improves business performance overall.

The most effective strategies will also be built to adapt. The reality of data and technology is that they are always evolving, and your data operations strategies should not only be built to account for that, but to support that change in other areas of the business.

The Cause of Data Silos

Disparate systems

Here are some key ways companies create silos through disparate systems:

  • Teams don’t fully understand the requirements of a data management or analytics strategy.
  • Solutions and tools are introduced retrospectively and don’t properly integrate with the system infrastructure or each other.
  • Board-level members don’t value data and don’t invest in the right technology.
  • Business-growth has overtaken technology growth.
  • Fear of updating systems because it is time-consuming or disruptive stops businesses from advancing.
  • Businesses are stuck in an old vendor contract and can’t update.

Cultural Data Silos 

Data silos can be caused in a number of ways – not just through software. Data management is the primary cause of silos, but often these issues develop because of data culture problems.

How can organisational culture cause data silos?

When data isn’t being shared between departments, teams or isn’t easily accessible to the right people, data silos arise. Yes, this could be because of isolated platforms, tools or poor data management. But more often than not, it’s actually a company culture problem.

Some common cultural issues that may create silos:

  • Teams don’t have the correct data skills
  • Departments and teams don’t communicate with one another
  • Management doesn’t value data insights
  • The organisation and business decisions aren’t data-led

This ends up with information and important business insight being isolated. Which ultimately undermines the value of data analysis, and encourages the problem.

How can you solve silos through culture?

Solving data silos starts with creating a more communicative and integrated workplace, that understands how to manage data. This makes it much easier to extract valuable intelligence which can be applied to business decisions.

Examples of cultural changes that eradicate data silos:

  1. Hiring people with the right data skills
  2. Training all staff on how to manage data and software
  3. Ensuring the board is engaged with data usage
  4. Encouraging a data-centric focus for the entire business
  5. Enabling greater communication between teams and departments
  6. Aligning inter-department data strategies


How can DataOps and DevOps help prevent data silos?

DataOps and DevOps are both approaches to business which will help you break down cultural and data silos.

DataOps focuses on data management and analytics, and aligns your data activity with key business objectives. This helps break down data silos by encouraging greater collaboration between teams, and better data organisation, and the integration of analytics with strategy.

DevOPs on the other hand is concerned with combining software development with IT, to help teams communicate better for faster delivery. By building a collective mentality and encouraging digital transformation throughout a company, DevOps quickly eradicates the cause of operational silos.

The benefit of Cloud Integration

How does cloud-based integration help solve data silos?

Cloud-based integration is built to break down silos.

The digital and data landscape is always changing, meaning companies constantly have to update or renew their data technologies. This is a reality that often results in organisations developing disparate systems and/or data silos. But cloud solutions are more future-proofed – enabling organisations to scale or evolve in a more flexibly way.

On-prem solutions often require manual data transfer or organisation. This inevitably leads to things slipping through the cracks and silos forming. Cloud solutions and storage instead connect to digital assets in real-time, meaning data is processed, transferred and shared instantly. Plus – it can be accessed from anywhere.

All of this has the purpose of increasing data visibility, unifying solutions, and mitigating silos before they arise.

What are the other benefits of cloud migration?

  • Access and process information quicker
  • Reduced operational and hardware costs
  • Works with your existing technologies and investments
  • Communications is made easier between teams
  • Improves cyber security by removing silos
  • Flexibility to change and scale your solutions


What about data security?

Poor security on cloud is a myth that seriously needs busted. Many people think cloud is inherently more unsafe than on-prem, but it simply isn’t true.

Firstly, data security issues on-cloud arise the same way as they do on-prem – by being poorly managed or neglected. The way to avoid this is simply to ensure your cloud settings are properly configured.

Secondly, cyber attacks are no more likely on-cloud than on-prem. Once again, as long as you have the right security solutions in place for your infrastructure – in this case, cloud-specific security, you’ll have nothing to worry about.


DataOps and Data Analytics  

Exploiting Machine Learning

Machine learning is an advanced analytics technique used to analyse massive amounts of information in order to turn them into actionable insights. Using models, it gathers data and establishes trends or patterns of normal behaviour.

How does machine learning fit into DataOps?

DataOps and machine learning are often used in tandem – with machine learning often integrated into DataOps and analytics tools, such as IT Operations Analytics (ITOA) and User and Entity Behaviour Analytics (UEBA).

The abilities machine learning techniques enable, such as prediction, anomaly detection, threat detection, and more, are essential elements for any data-driven business or analytics model. It adds intelligence to the processing and organisation of data – making connections between huge amounts of raw data in order to gain meaning and intelligence.

In turns, DataOps supports machine learning capabilities by building the right environment and data streams. This means your machine learning tools are more likely to receive the correct data to make better, more reliable predictions.

What are the benefits of machine learning?

  • Anomaly detection: Helps detection anomalous or malicious behaviour which can be used to identify cyber security threats or breaches.
  • Greater efficiency: Helps automate data analysis and the generation of business insights for the benefit of operations, strategic development, cyber security and more.
  • Increased Intelligence: The more data these tools get, the more they learn, and the more accurate they become.

 What are the challenges?

  • Not having the right infrastructure in place to support these tools
  • Teams don’t have the experience in analytics/machine learning
  • Your existing tools don’t integrate with machine learning solutions
  • Disparate systems don’t support machine learning effectively
  • Many data stores are inaccessible due to data silos
  • Takes time to integrate the machine learning processes and outputs into the business

Predictive Analytics and ITOA

What is ITOA?

ITOA stands for IT Operational Analytics and is the act of collecting and analysing data from all areas of a business to produce a single view of all activity. This approach aims to harness big data (i.e., data that is constantly changing and growing) to improve data operations and break down data silos.

This process brings together all a business’s devices, networks and more, in order to eradicate disparate systems. This way, all data sources are connected, and data can be gathered together into a single place.

What are predictive analytics used for?

ITOA uses business data, both historical and current, to make predictions. These outputs can be applied to business use cases and IT operations to improve performance and customer experience, or analyse potential cyber security threats, respectively.

Some area use cases include:

  • Fraud detection
  • Trading structures
  • IT Operations
  • Churn and retention
  • Predictive maintenance
  • Risk assessment
  • Risk modelling
  • Quality assurance
  • Cyber security analytics

What is the benefit to DataOps?

  • Identifying and mitigating incidents in advance
  • Drives better system performance and operations
  • Reduces operational overheads
  • Automatic detection capabilities
  • Decreases response time
  • Problems are resolved quicker
  • Connecting disparate systems and removing data silos
  • Improves customer experience

Data Visualisation Tools for DataOps

DataOps doesn’t work without some level of data visualisation.

Data visualisation tools are necessary for businesses to understand their data which in turn supports better data processes. With the right insight, teams can plan in advance, create better data management strategies, integrate their workflows with other departments, and improve performance in many areas. These insights are only gained using the right visualisation tools.

What are some data visualisation use cases?

The list is endless. From pie charts in your monthly meetings, to daily manipulation of interactive dashboards, data visualisation can be applied to nearly anything and everything.

There are many DataOps tools that are specifically designed to be supported by data visualisation; providing a holistic view of all activity in order to eliminate data silos and disparate visualisation. They can also be used to enhance DevSecOps; ensuring proper data visibility in order to improve cyber security, for example by finding links in behaviour or anomalies.


Example data visualisation tools:

  • Splunk: Integrated visualisation to support data interaction, view customisation and data analysis.
  • Exabeam: Empowers users to identify outliers using Big Data visualisation and analysis.

What is advanced data visualisation?

Advanced data visualisation involves drilling down into the minutiae of your data. By visualising individual data points, times, or places, users can find the specific answers they need, such as the root cause of an anomaly.

Advanced data visualisation gives users more control over their data and what they see. This is all for the purpose of gaining greater insight and actionable intelligence.

DataOps and Cyber Security

The effect of data silos on cyber security

Big data can have huge benefits for businesses. It helps inform business decisions, marketing, product strategies and more. The more information a business has about its customers, the more effectively it can target its audience.

However, if your data isn’t managed properly it can end up causing more harm than good. One of the primary risks that comes with big data is the creation of data silos.

What are the biggest challenges with business intelligence?

Drawing business intelligence from big data lakes is not an easy task. It’s important to consider these key challenges when working with big data:

  1. Volume and Size of Data

The sheer amount of data available makes it difficult to identify actionable intelligence without a targeted approach.

  1. Data Silos

When databases become disorganised or disconnected, data silos are made which prevents users from easily accessing data.

  1. Security and Privacy

Big data stores are a primary target for hackers, and data silos or disorganisation makes databases even less secure.

How can data silos cause cyber security issues?

Data silos are essentially an open back door for hackers. They’re a sign of vulnerable cyber security systems, and hackers know to look for them. They’ll exploit these gaps in security to steal and exploit data.

Silos between networks, databases, or security systems make it more difficult to identity large-scale attacks. They also tend to represent disjointed departmental protocols. When teams aren’t communicating, their security protocols are more likely to be inconsistent and there could have weaknesses. 


How can you combat data silos to improve cyber security?

In a word – DataOps. Implementing data operations will help you eliminate silos and create a more robust cyber security system. It involves identifying existing silos and why they’re happening, then introducing strategies to fix this such as merging information systems.

It isn’t a quick fix – but it is the most effective. With this, you can ensure all data and security protocols are consistent across the company, giving you a stronger cyber defence.


UEBA for Threat Detection

What is UEBA?

UEBA stands for User and Entity Behaviour Analytics. It is an approach to cyber security that focuses on creating a comprehensive picture of system, device, and user activity. This is done by tracking cyber activity daily and establishing patterns that are considered ‘normal’.

Based on these patterns built with historical data, UEBA tools can detect suspicious or unusual activity that could be a potential cyber threat. It will then analyse this activity to gather more information and determine whether it is malicious or not.

Two key techniques that enable this:

  1. Machine learning
  2. Big data analysis

How does UEBA improve threat detection?

 UEBA enables real-time threat detection and instant responsive action. While previously detection methods have been reactive, teams can use UEBA to proactively detect threats as quickly as possible – or even to identify potential attack patterns in advance.

Ultimately, cyber security practices become much more informed and effective when backed by UEBA.


What are the benefits of UEBA?

  • It enables proactive cyber security practices
  • Improves response time to real-time attacks
  • Reduces the risk associated with attacks
  • UEBA uses machine learning to consistently improve
  • Reduces false positives and evaluates whether an anomaly is malicious
  • Helps prioritise attacks by criticality
  • False positives are reduced
  • Provides automatic alerts to abnormalities, changes, or unidentified behaviour
  • Identifies insider and external attacks

GET IN TOUCH with someone from the RiverSafe team to find out more about how we can help you with DataOps.


By Riversafe

Experts in DevOps, Cyber Security and Data Operations