The fast pace of the market and the competition make business management unpredictable unless you have the right tools like data-driven business intelligence (BI).
By providing insights into market patterns, buyer behaviour, and other economic factors, business intelligence can help you make better decisions to improve business performance. BI tools allow you to explore large datasets and leverage them as a resource to gain useful insights.
By leveraging BI tools, you can enjoy improved efficiency, fraud identification, better product management, improved brand image and more.
While there are many BI tools in the market, SAS has always been a leader in data analytics. With AI-driven platforms that provide you with an extensive range of tools to enhance your data analytics capabilities, SAS can help you streamline your business processes.
Here are the reasons why you should choose SAS analytics business intelligence and data management for your brand.
While traditional data analytics tools can deliver quality insights, most often than not, they fail to deliver these insights to all parties in the decision-making process.
That said, with SAS, you can overcome this challenge and improve information access across your business functions.
One of the key features of the SAS business intelligence suite is the ability to easily integrate with MS Office tools like Excel and Outlook.
Through this integration, you can distribute information and exchange important insights with others involved in the decision-making process. Storyboard and narrative creation features available in the platform assist with presenting data to decision makers in an understandable manner.
In addition, all these tools access data through metadata representations, making it easier for everyone involved in decision making to receive quality insights and orderly create action plans.
Navigating a data management system and analytics tools is not always straightforward unless you are well-equipped with the knowledge of information technology. Most of the time, you will have to rely on IT pros when managing your data, making the whole process time-consuming.
With the SAS business intelligence platform, you have access to integrated tools that perform multiple functions like analytics and reporting, making it easier to navigate. This also allows you to access and manage data, make decisions and draw inferences without relying on IT professionals.
With visual data analytics, you will also have valuable data represented in graphs, charts, and other visuals, making information and insight gathering convenient and comprehensive.
Additionally, the Business Intelligence app gives you 24/7 access to business functions with devices such as smartphones—you can monitor your business from anywhere, anytime.
SAS analytics business intelligence and data management ensures accuracy and high precision in functions like predictive and descriptive modelling, forecasting, simulation, and experimental design.
As a result, you can leverage SAS to build an effective analytics strategy and formulate data-driven decisions to improve your marketing, accelerate your operations, or enhance the customer experience.
The focus on consistency and standardisation of data also allows you to avoid erroneous or false data that could lead to wrong decisions that can endanger your business.
Today, the business environment is more challenging than ever before. You need the right tools to survive and succeed in this landscape, and SAS Analytics helps you do that.
Here at Selerity, we are committed to providing you with a seamless SAS experience through our range of managed services.
Don’t hesitate to contact our team to learn more about SAS analytics business intelligence and data management.
When you think about data analytics, we think of the technology necessary for analysis, like NLP, but we don’t give much thought to data preparation. Preparing data is not the most exciting function when mining big data for additional insights, but it is still a crucial step in the data analysis process.
As you may have guessed, this is the stage where data experts curate and prepare data. They have to comb through the different sources to find the relevant facts and prep them for analysis. Preparing data is one of the most important functions one can do. Without it, businesses would be working with flawed data, which compromises business findings. After all (and I am sure I have mentioned this before), you can have the most sophisticated data analytics technology, but without accurate data, you will only create tainted findings, which will compromise decision-making.
Yet despite its importance, several organisations struggle to address data preparation challenges.
Let’s take a look at some of the challenges organisations face when optimising data.
Data experts/consultants are some of the most expensive people an organisation can hire. Surely this means that corporations are going to generate the most value out of them by assigning them only high-end tasks? Wrong.
A lot of data scientists spend a good amount of their time shifting through different data sources. In fact, data experts spend as much as 60% of their time cleaning data and preparing it for analysis. It is a huge waste of resources, considering that the skills and knowledge of data analysts can generate a lot more value if they focus on more high-end work. The misallocation of talent is one of the biggest challenges an organisation faces during the data preparation stage.
Certain organisations lacking mature data analysis practices neglect context. This occurs because the IT and business departments are not in sync in their objectives. IT analysts spend several cycles curating the perfect dataset only to find that it lacks the relevant context, rendering it useless.
Incorporating context is a challenge in data preparation because it requires collaboration between different departments or business units. Resolving this particular challenge requires the mobilisation of resources and the expertise of different professionals working together towards a common goal. This is not impossible to accomplish, but it takes time to resolve because different stakeholders need to be persuaded on the value of analytics.
However, one of the biggest challenges for any organisation is to spot and fix data quality issues. Data quality is determined by the level of consistency, conformity, relevance and completeness. This challenge can be attributed to the immense data volume organisations have to process regularly. Given that most organisations are dealing with petabytes of data, spotting quality issues becomes a huge challenge – especially without the right equipment.
Fortunately, data preparation challenges are not insurmountable obstacles and as data analytics plays a bigger role in revenue generation, organisations will have to take the time and effort to address the challenges that hinder data preparation and analysis.
However, what is the best way to resolve these challenges? In my experience, there is no one single solution. Resolving data preparation challenges means investing in the right technology, mobilising the organisation’s resources, and on some occasions, a complete restructuring of operations to eliminate inefficiencies in data preparation. Through judicious planning and resource mobilisation, organisations can resolve their data preparation challenges.
Looking to resolve data preparation challenges? The technology you have will be a huge contributing factor in eliminating the challenges that plague data analysts. With the right data analytics platforms, organisations will have a much easier time resolving data preparation challenges and optimising the entire process.
Once the data collection and analysis process has been optimised, cleaning data and making sure it is accurate will be significantly easier than before. Addressing data preparation challenges leads to other benefits like more accurate data analysis. When data is clean, businesses can be assured that their reports will be accurate, which boosts confidence in decision-making.
Data lakes are the key to streamlining data collection and analysis. However, there is no denying the obvious benefits of these lakes but, like most technologies, there are some disadvantages to using a data lake. It’s important for organisations to be aware of its shortcomings before investing in it. This blog post attempts to address some of the problems that come with data lakes. If not implemented properly, the lake could end up hurting the organisation more than benefiting it.
There are several technical and business challenges of using data lakes.
Data lakes are an open-source of knowledge designed to streamline the analytics pipelines. However, the open nature of the lake makes it difficult to implement security standards. The open nature of the lake and the rate data is inputted, makes it difficult to regulate the data coming in. To eliminate this problem, data lake designers should work with data security teams to set access control measures and secure data without compromising loading processes or governance efforts.
However, it’s not just security that’s causing problems with data lakes. It’s also an issue of quality. Data lakes collect data from different sources and pool it in a single location, but the process makes it difficult to check data quality. It is problematic because it leads to inaccurate results when the data is used for business operations. When the data is inaccurate, the findings will be inaccurate, causing a loss of confidence in the data lake and even in the organisation. To resolve this problem, there needs to be more collaboration between data governance teams and data stewards so that data can be profiled, quality policies implemented and have action taken to improve quality.
Metadata management is one of the most important parts of data management. Without metadata, data stewards (those who are responsible for working with the data) would have little choice but to use non-automated tools like Word and Excel. Moreover, data stewards spend most of the time working with metadata, as opposed to actual data. However, metadata is not implemented on data lakes, which is a problem, in terms of data management. The absence of metadata makes it difficult to perform vital big data management functions like validating it or implementing organisational standards. Since there is no metadata management, it becomes less reliable, hurting its value to the organisation.
Data lakes are incredibly useful, but they are not immune to clashes within the organisation. If the organisation’s structure is plagued with red tape and internal politics, then little value can be derived from the lake. For example, if data analysts cannot access the data without obtaining permission, then it holds up the process and hurts productivity. Different departments might also have rules for the same data set, leading to differences in rules, policies and standards. This situation can be somewhat mitigated by having a robust data governance policy in place to ensure consistent data standards across the whole organisation. While there is no denying the value of data lakes, there need to be better governance standards to improve management and transparency.
Identifying data sources in a data lake is not often done, which is a problem in big data management. Categorising and labelling data sources is crucial because it prevents several problems like duplication of data. Yet, this is not done regularly, which is problematic. At the very least, the source of metadata should be recorded and available to users.
Big data management is made much easier with the use of data lakes. However, there are some challenges when it comes to using the centralised repository. These challenges can hinder the use of the data lake because it becomes harder to discover actionable insights when the data is flawed. If there is a problem with the data, then insights are useless. The main challenge of fixing these problems is implementing multi-disciplinary solutions. Fixing problems with data lakes requires comprehensive technical solutions, adjusting business regulations and transforming work culture. However, organisations need to address these problems. Otherwise, they will fail to draw maximum value from their data lakes.
Integrated analytics is the key to improving data management and analysis. While public and private organisations are interested in analytics, they have yet to fully embrace its full potential. Gartner predicts that private organisations not using data analytics in 2020 will be out of business in 2021. While public organisations don’t have to worry about going out of business, if they don’t advance their analytics capability, they will lose out on its benefits, like smarter resource allocation and lower operating costs. All this means that more companies are open to using analytics to make sense of all the information they generate. However, that alone is not enough, public and private organisations must take their analysis a step further with integrated analytics.
Data management is crucial for success in the modern era. So it stands to reason that organisations want to maximise the management of their potential with integrated analytics.
Integrated analytics sets the foundation for more efficient data analysis. While some analytics platforms make analysis easier, they are not integrated into data management software. The silos between analytics and software create several problems for the business. Having data analytics as a separate layer hinders productive efficiency because integrating data into management software is an extra step that can be easily avoided. Furthermore, there could also be compatibility issues between the data management software and the analytics platform, forcing data analysts to shift their focus away from analysis and towards fixing the problem (or at least working around it).
However, when analytics is integrated into data management, it makes for more efficient, timely analysis because integrated analytics removes problems, and streamlines the analytics process, allowing analysts to spend more time analysing data and less time working around system limitations. Indeed, most organisations are looking for analytics platforms that can integrate with already existing software, instead of changing their entire infrastructure to suit the analytics platform.
Data analytics alone is not enough for private and public companies. They need analytics platforms to provide real-time insights. Real-time analytics is crucial because it provides a continual feedback mechanism to organisations and removing any silos in the analysis process. Flexible, real-time analysis allows organisations to future-proof their analytics capabilities because they are in a better position to respond to sudden and structural changes. Integrated analytics provides real-time feedback because it is embedded in the data management software. When analytics is embedded, processes like tagging, indexing, data migration and categorising can be done smoothly. When analytics is integrated into data management, it makes real-time analysis easier.
When data analytics is more flexible, it allows organisations to target their analytics mechanism to answer only relevant business questions. Organisations need more than just analytics and data management software. They need flexible analytics software. The flexible nature of integrated analytics allows organisations to constantly monitor the data they are collecting and configure them when necessary. This is due to the continuous feedback mechanism provided by real-time analytics. Data analysts can then tweak the data collection and feedback mechanism to answer the most important business questions.
Furthermore, it becomes much easier to present the information to business executives who don’t have a background in data analytics.
The ability to refocus business analytics to answer relevant business questions allows organisations to expand their insights and get a more holistic view of business operations. Most business analytics software can go-in depth into a single metric like revenue generated. It allows corporations to see which salesperson is generating the most money. However, by integrating analytics into data management, organisations are in a better position to collect data from different sources to get a better understanding of sales operations and activities. For example, instead of looking at revenue generated by a salesperson, organisations can see who has the most prospects for future sales or has the most positive feedback, to get a better picture of the overall sales operations.
The days when data analytics were a completely separate platform from data management is rapidly coming to an end. Most organisations, be it private or public, want an analytics platform that can integrate effortlessly into the data management software because it makes analytics easier to work with, streamlines the entire process and eliminates unnecessary steps. Integrated analytics provides everything businesses are looking for in an analytics platform: Actionable, real-time insights without any of the lag in integrating it into their data management software, making it a must for businesses.
Missing data is an inevitable part of the process. As data researchers, we pour a lot of resources, time and energy into making sure the data set is as accurate as possible. However, data inevitably goes missing. As someone who has been handling data analytics and overseen dozens of research projects for several years, missing data is just one of those “It sucks, but it’s no one’s fault” scenarios. Sometimes, data sets come up short, no matter how many times data scientists clean and prepare it. The best way to handle such situations is to develop contingency plans to minimise the damage.
Missing data is a huge problem for data analysis because it distorts findings. It’s difficult to be fully confident in the insights when you know that some entries are missing values. Hence, why they must be addressed. According to data scientists, there are three types of missing data. These are Missing Completely at Random (MCAR) – when data is completely missing at random across the dataset with no discernable pattern. There is also Missing At Random (MAR) – when data is not missing randomly, but only within sub-samples of data. Finally, there is Not Missing at Random (NMAR), when there is a noticeable trend in the way data is missing.
The deletion methods only work for certain datasets where participants have missing fields. There are several deleting methods – two common ones include Listwise Deletion and Pairwise Deletion. It means deleting any participants or data entries with missing values. This method is particularly advantageous to samples where there is a large volume of data because values can be deleted without significantly distorting readings. Alternatively, data scientists can fill out the missing values by contacting the participants in question. The problem with this method is that it may not be practical for large datasets. Furthermore, some corporations obtain their information from third-party sources, which only makes it unlikely that organisations can fill out the gaps manually. Pairwise deletion is the process of eliminating information when a particular data point, vital for testing, is missing. Pairwise deletion saves more data compared to likewise deletion because the former only deletes entries where variables were necessary for testing, while the latter deletes entire entries if any data is missing, regardless of its importance.
Regression is useful for handling missing data because it can be used to predict the null value using other information from the dataset. There are several methods of regression analysis, like Stochastic regression. Regression methods can be successful in finding the missing data, but this largely depends on how well connected the remaining data is. Of course, the one drawback with regression analysis is that it requires significant computing power, which could be a problem if data scientists are dealing with a large dataset.
Data scientists use two data imputation techniques to handle missing data: Average imputation and common-point imputation. Average imputation uses the average value of the responses from other data entries to fill out missing values. However, a word of caution when using this method – it can artificially reduce the variability of the dataset. Common-point imputation, on the other hand, is when the data scientists utilise the middle point or the most commonly chosen value. For example, on a five-point scale, the substitute value will be 3. Something to keep in mind when utilising this method is the three types of middle values: mean, median and mode, which is valid for numerical data (it should be noted that for non-numerical data only the median and mean are relevant).
Missing data is a sad fact of life when it comes to data analytics. We cannot avoid situations like these entirely because there are several remedial steps data scientists need to take to make sure it doesn’t adversely affect the analytics process. While these methods are helpful, they are not foolproof because they are contentious, meaning, their effectiveness depends heavily on circumstances. The best option available to data scientists is to work with powerful, processing tools that can make the data capturing and analysis process significantly easier. It is the best way to handle missing data.
Missing values can be an inconvenience during data analysis. Fortunately, SAS comes with a MISSING function to check for these values. With the Selerity analytics desktop, you will have access to useful features like this and more. If you want to learn more about SAS, get in touch with us.
With the emergence and subsequent dominance of the internet, the amount of touchpoints businesses have with their customers has multiplied considerably. Social media, websites, blogs, forums, mobile devices – the list goes on and on. On these platforms, a gargantuan amount of data is created every day. If this data is properly stored and analysed, it could provide organisations with invaluable data regarding user behavioural patterns, preferences and even insights into their competitors. However, due to the sheer size and volume involved, big data management has been a challenge for most enterprises within the last decade – that fact has changed over the past few years.
With the introduction of new applications and techniques like cloud management, an increasing number of businesses have embraced big data. As a result, big data management and the insights it delivers have become the basis for many organisational processes, including decision making.
To start off with, it’s important to understand how big data analytics is utilised for decision making. While from the offset it may seem like a mystical process, the collection of big data analytics and their utilisation in decision making isn’t all that complicated.
Goals are identified by the business initially. These will be the benchmarks you use to test performance and identify whether the business is heading in the right direction. Once the goals and performance metrics are identified, it’s good practice to refine them. This ensures that only the best data is collected and that your analysis is ultimately better.
Following this, the most important step in big data management occurs – the data collection. The goal here is to use as many relevant sources as possible; as we said earlier, with the abundance of customer touchpoints, this shouldn’t be an issue. Data compiled can either be structured or unstructured and it will be up to the software you’re using to make sense of all this.
All collected data should subsequently be refined, and be categorised based on their importance for achieving the goals identified earlier. After unnecessary data is weeded out, it’s imperative to segregate everything based on what their purpose will be – is this going to help improve efficiency? Will this help improve consumer relations? And so on.
Once the data has been prepped it’s time to start analysing and applying. Here it’s imperative to choose the right tools and software for your big data management, as they can reap great benefits for your organisation. And now you’ll have your valuable insights, meaning you’ll be ready to execute strategies and make decisions based on them.
So, with everything set for you to start utilising big data in the decision-making process, what’s next?
For most organisations, the crux of their operations revolves around the relationship they maintain with their consumers. Strengthening and building upon it often serve as the key to a business’s successes. It’s a pretty simple equation – the more engaged your customers are with your product and brand, the better your conversion rate is going to be. This simple fact makes the goal of customer-related decisions relatively straightforward – ensure they are engaged and that you retain them.
Big data management provides the opportunity to do just that. Effectively utilising big data reveals previously unidentified trends and patterns about your consumers. This includes their buying patterns, product partialities and even the relationships they have with your competitors. With this information in hand, organisations can begin crafting tailored content – from product launches to full-blown marketing campaigns – for your consumer base.
All organisations strive to be more efficient. Decisions are always being made with the goal of improving performance in both the workforce and in everyday processes. The issue is, it’s not always inherently clear what the best choices are; it isn’t uncommon for organisations to resort to trial and error to identify the best practices. Big data is able to demystify all of this, however. With big data management, the outcome of efficiency-related business decisions can be calculated fairly precisely on a real-time basis.
Automation has also become a preferred option for many businesses looking to improve their efficiency. This even includes automating the decision-making process itself – and this is a data-driven affair. By melding big data with automation software, organisations can create a system that streamlines the decision making process and subsequently boosts work efficiency.
Companies always have a plan to grow; to expand their services, grow their consumer base and raise their brand image. The decision-making quandary with expansion is the investment that it requires. Once again, big data management alleviates this issue. Think of all the optimisation possibilities that are uncovered with effective utilisation of big data. Now add all the consumer engagement and retention opportunities it delivers. Simply put, decision-making brought about by the real-time analysis of data will create natural growth for your business, with no need for any additional investment.
As such, the role big data management plays in the organisation decision-making process is apparent – it’s a vital tool that eases the pressure and doubt that surround major business decisions. Effectively using big data when making decisions is near-guaranteed way to build better relationships, foster a better work environment and facilitate healthy growth for an organisation.
The key to improving business outcomes is knowing how to manage data. Organisations can invest in analytics platforms with the latest AI technology or invest in better data infrastructure. However, without proper data management, they will not realise the desired business outcomes. Data management is one of the most important processes a business can invest in. It is the key to improving operational efficiency and developing smarter, more effective business plans. In the past, we have talked about a data governance framework and its role in data management. In this blog post, we are going to discuss data profiling and data cataloguing for managing data.
When discussing ways to manage data, we need to take into account the two storage formats for data: Data warehouses and data lakes (more and more organisations are shifting to the latter), though they exist as a repository for data, how the data is collected and passed on for analysis is very different.
Data profiling helps organisations manage data better by categorising, naming and organising information. It involves running a diagnosis and examining data to check for inconsistencies between data categorisation and how it is labelled. Data profiling is a visual assessment that relies on business rules and analytical algorithms to check if data has the right format properly integrated into the system.
As the name implies, data cataloguing is the process of naming all the data elements found in the data lake. The idea is not to add an extra layer to conform the data but to manage data better by giving users the means to know and search for data elements stored in the data lake. Data cataloguing is not a new technique, but it has seen a resurgence due to the growing prominence of data lakes and the proliferation of automation technology. With data cataloguing, users are free to look at the data lake, no matter their technical expertise because vendors can create sophisticated tools that make the search process much easier than ever before. Cataloguing is particularly well-suited to manage data in data lakes because it makes the information accessible without compromising the open nature of data lakes.
Data profiling comes with several benefits organisations need to manage data better. The foremost benefit is the improvement in data quality, thanks to higher data consistency and more accurate readings. Profiling data makes it more credible because it eliminates any errors and accounts for missing values and outliers. It improves data management by centralising and organising company information. Moreover, data profiling has an immediate effect on business outcomes because it reveals surrounding trends, risks, opportunities, as well as, expose areas in the system that suffer from data quality issues, like input errors and data corruption.
Data lakes are very useful for streamlining data processing, governing data and developing new analytics models. However, continuously dumping data turns a data lake into a data swamp because adding data without criteria robs it of all clarity. Fortunately, data cataloguing can help categorise data in a data lake. Data catalogues help manage data a lot better due to its tagging system. It unites both structured and unstructured data through a common language with definitions, reports, metrics, models and dashboards. This unifying language is important for improving data management in a lake because it helps set relationships and associations between different data types, which could prove invaluable in the future. Besides, the unifying language allows non-technical professionals to understand the data in business terms.
Data catalogues help manage data because users can easily find what they are looking for with the catalogue. Essentially, a catalogue will allow users to find the precise data items they are looking for to make the analysis more efficient. Even better, data is more accessible because anyone within the organisation hierarchy can access the data they need. Furthermore, a catalogue improves trustworthiness within the organisation because it provides assurances that data is more accurate and reliable. Finally, cataloguing data makes the entire process of analysing more efficient than before because it makes finding data items much easier.
To profile data effectively, data analysts have to know about the three different methods of data profiling. The first type is relationship discovery, where analysts find connections, similarities, associations and differences between data sources. The other type of data profiling is structured discovery, where the focus is on formatting the data to make sure data in the warehouse is consistent across the board. This type of discovery uses basic statistical analysis to return information about the validity of the data. Finally, content discovery assesses the quality of data, by identifying incomplete, ambiguous and null values. Understanding the different data profiling methods is crucial to profile and manage data.
To start profiling data, it is gathered from multiple sources and the metadata is collected for analysis. Once data is collected and cleaned, profiling tools will be used to describe a dataset. The tools will evaluate the content to find existing relationships between value sets across data.
Of course, data profiling can be done in different ways to manage data: Column, cross-column and cross-table. Column profiling refers to the number of times a value appears in a column within each table, helping to uncover patterns within the data. Cross-column performs key and independent analysis to determine the relationships and dependencies within a table. Crosstable profiling determines which data can be mapped together and what might be redundant. The data is determined by finding the similarities and differences between syntax and data types in tables.
Automation plays a huge role in the creation of a data catalogue to manage data. However, creating a catalogue starts with accessing the metadata. Data catalogues use metadata to identify databases, data tables and files. The catalogue crawls through the company’s databases to bring the metadata to the data catalogue.
The second step to managing data with a data catalogue is to build a data dictionary, the dictionary contains descriptions, and detailed information on every table, file and metadata entities. Once the dictionary is complete, developers should profile the data to help users view the data quicker. The next step is marking the relationship – developers discover related data across multiple databases. Related data can be marked in different ways, like advanced algorithms and query logs from developers.
The next step is building a lineage, which can help trace data from its origin back to its destination. Data analysts will use this lineage to trace an error back to its cause. Then, the data needs to be extracted from the source and transferred to databases for cleansing. The process is known as ‘Extract, Transfer, Load’ or ETL. Once the data is loaded it should be arranged. Organising data can be done using several methods like tagging, automation and organising the specific usage. Machine learning (ML) models are integral to building a data catalogue because they can work with large data volumes. ML models can identify data types, relationships and incorporate information to increase accuracy. Machine learning models can help build a data catalogue at a faster rate and with greater accuracy, compared to more conventional methods.
Of course, it is important to keep in mind that there will be some challenges to profiling data and setting up data catalogues. For example, organisations have to account for unstructured data when setting up their catalogue and data profiling is very difficult to do – especially if there is a large volume of data to work with or if legacy systems are used. However, regardless of the challenges, there is no denying that profiling and cataloguing are two of the best ways to manage data. With proper profiling and cataloguing, the process of collecting and analysing information is made more efficient and easier to manage.
When organisations properly manage data, they have a clearer picture of the type of data they have in store, which gives them a better understanding of their strengths and weaknesses. Properly organised data improves the rate at which insights are generated because only the most relevant can be parsed for analysis. Irrelevant data will just muddy the results. Furthermore, the entire process will be more efficient because data is properly organised, improving operational efficiency.
Don’t just focus on data!
While organisations should take care to manage data with a comprehensive data framework. They should also optimise their data analytics platforms to improve the quality of findings and reduce overhead administration costs. Working with analytics experts and specialists can help organisations cut costs because they do not have to shoulder the technical and administrative burden of installing, administering and hosting analytics platforms. Analytics specialists can also find ways to optimise the analytics platforms to make it function more efficiently than before, in a manner that is more tailored to your requirements. Hence, organisations should manage data and invest in their data analytics environment to get the best outcomes.
In the IT industry, it is well-known that data is more precious than oil. Yet, despite the immense value of data, many organisations do not make full use of their data. There are several reasons behind this – lack of initiative from executives is one example. But, one of the core problems I have discovered is the lack of a comprehensive data governance framework. With this framework in place, organisations address all aspects of their data management, ranging from their practices to technologies in use. In this blog post, we are going to take a look at the key essentials of a data governance framework and why organisations need to have one.
The value of a data governance framework
A data governance framework is a must-have for every organisation because it helps manage the growing volume of data. Data utilisation is growing at an exponential rate with organisations collecting petabytes of data daily. However, without a framework to collect, integrate, clean and analyse data, it is impossible to manage the growing volume of data and derive meaning from it. Data is immensely valuable, but like coal or oil, it cannot be used in its raw state, it needs to be cleaned and refined before it can be useful. However, without a framework in place, making full use of data becomes impossible to do regularly.
A data governance framework allows organisations to make a direct connection between data and KPIs or corporate drivers. The best way for organisations to generate the most value from their data is to tie it to company fortunes. That way, organisations can objectively measure progress on long-term and short-term goals. To tie data to an organisation’s fortunes, there needs to be a direct connection between data and corporate drivers. However, finding the connection can be very challenging without a data governance framework. An appropriate framework allows organisations to make direct correlations between data and corporate drivers like operational efficiency, profitability and costs.
As the public becomes more and more aware of data usage, there will be pressures to be more responsible and transparent in data usage. The first signs of data governance from government institutions can be found in the Sarbanes-Oxley Act in the US and the General Data Protection Regulation (GDPR) in the EU. In the future, I fully expect governments to keep a closer eye on how corporations use data. With a growing focus on data regulation, a data governance framework can be instrumental in ensuring that the organisation is complying with data laws.
The key elements of a framework
In setting up a data governance framework, organisations will need to reexamine everything from their policies to their attitudes towards data.
Categories for the data governance framework include but are not limited to corporate drivers, principles of using data, objectives behind data, groups for new data governance programs, methods for data usage, processes behind data usage, management structures, data management technologies and data governance methods.
A data governance framework entails a holistic shift for the organisation, which means several stakeholders need to work together. Executives and IT professionals in the organisation must cooperate to define the rules that will govern the use of data in applications. The two sides must define the use and management of data from data models, databases and even individual technology (for example, computers and laptops). They must also address processes and day-to-day use, especially for creating and using data. The key parties must also consider how the rules should be implemented so that rules do not hinder data creation and analysis.
How to get started
Building a data governance framework might seem like an impossible task, but organisations can take solace from the fact that these frameworks are not built from scratch. Most organisations, be it big or small, already have some sort of framework for their data. In most cases, the organisation only needs to adjust practices at certain steps or upgrade their existing technology. One such technology is the hosting environment. The right hosting environment is a tremendous asset in setting a data governance framework because it makes data management more efficient. Some organisations can even host their data on a cloud-based environment, physical servers or a combination of both.