Connect with us

Featured

When data need not intimidate

Published

on

As more data sources become available, businesses often struggle to manage them. However, proper data management starts with a solid understanding of data governance which many companies think is an intimidating task, writes ANTIONETTE VAN ZYL.

Market forces are driving data awareness as businesses realise that they can derive significant value from effectively analysing data and applying the findings to decisions and actions, and as regulators tighten rules around how data should be managed.

‘Big data’ is still used as a buzzword in business. But data has always been available – it’s just evolving as more data sources become available, such as cloud, mobile and click-stream data. And with the growth of machine-to-machine technology and the Internet of Things, even more data sources will come online soon. So how do we manage these new data types?

Proper data management starts with a solid understanding of data governance. Businesses also need strong policies that enforce rules regarding data management. Effective data governance involves people, processes and technology to ensure consistent and proper handling of data. It involves all levels of data processing, including data management, data quality, policy management, business process management and risk management.

Data should be clearly defined, secure and fit for purpose if a business wants to derive benefit from it. To achieve this level of data reliability, policies should specify how data should be captured. This quality control measure ensures that any data issues are corrected at the source and that information assets are formally managed throughout the enterprise.

Effective data governance practices require support from executive management if they are to be successful. However, many CEOs do not link data to business value, believing that data is an IT issue, while IT believes it merely supplies the data to the organisation.

Another challenge when implementing data governance strategies is that different departments within an organisation have different agendas when it comes to data. As a result, they may each have their own processes for managing data, resulting in siloed systems that don’t communicate with each other and are difficult to integrate.

There is a perception that data governance is a massive and intimidating task. Businesses know they should be doing it but they don’t know where to start. Data governance doesn’t need to be applied to the entire organisation in one fell swoop. Rather, when embarking on the data governance journey, businesses should start small – in a single department. Data governance requires change – change in mindsets and change in processes. It’s much easier to convince staff and executives of the business value of data governance if benefits can be shown in a single area and expanded from there.

Data governance framework

So where do you start? Below, I have outlined a top-down data governance framework that will assist any business in establishing a single, consistent set of policies and processes for managing data. The good news is that data governance is not a linear process – businesses can start from the top, the bottom, or somewhere in the middle. My advice is to start with those areas that are already in place and work from there.

Plan

Determine the business’ data governance readiness. Identify current high-impact projects and upcoming initiatives and link these to a strategic initiative. For example, one business strategy could be to increase customer retention numbers through a loyalty programme and setting up social platforms to engage with customers. Initiatives to achieve this could include using analytics to anticipate customer need based on behaviour trends and to tailor offers and communication to those needs.

Next, assemble a core working team that will provide oversight, manage risk and assess compliance. This group of visionaries will define the data governance charter, including the business mission, key benefits and guiding principles.

Design

Identify an initial target project, such as a customer loyalty programme. A data governance council is decided at this stage, which will serve as the main decision-making body on the project. It will also determine the decision rights, list key decisions, engage other decision-making bodies and assign accountabilities.

It’s important at this stage to refine and formalise data management – this is where IT will be roped in.

Execute

Go forth and launch your data governance process! Key to ongoing success is to continually measure and refine the process, monitor progress and report issues or risks. At this stage, data governance should be absorbed into the software development lifecycle so that it forms part of all processes going forward.

Poor data governance can cause many headaches for businesses, including poor customer service, limited upsell/cross-sell opportunities, an inefficient supply chain, an inability to automate key processes, poor operational planning and execution, and, importantly, exposure to fraud and other risk.

On the other hand, efficient data governance systems present a single platform on which all different roles and departments can be supported, allowing for the enforcement of central policies and monitoring of those policies. As a result, information is treated as a business asset and is readily available to support evidence-based decision-making – this saves time as the business knows the data can be trusted and does not need to be verified.

Ultimately, the business is able to make decisions faster, its information is consistent and aligns with values and goals, and risk management is improved – all because of collaboration and clean, valuable data.

* Antionette Van Zyl, Senior Solution Manager: Data Management at SAS

Featured

IoT at starting gate

South Africa is already past the Internet of Things (IoT) hype cycle and well into the mainstream, writes MARK WALKER, associate vice president of Sub-Saharan Africa at International Data Corporation (IDC).

Published

on

Projects and pilots are already becoming a commercial reality, tying neatly into the 2017 IDC prediction that 2018 would be the year when the local market took IoT mainstream. Over the next 12-18 months, it is anticipated that IoT implementations will continue to rise in both scope and popularity. Already 23% are in full deployment with 39% in the pilot phase. The value of IoT has been systematically proven and yet its reputation remains tenuous – more than 5% of companies are reluctant to put their money where the trend is – thanks to the shifting sands of IoT perception and success rate.

There are several reasons behind why IoT implementations are failing. The biggest is that organisations don’t know where to start. They know that IoT is something they can harness today and that it can be used to shift outdated modalities and operations. They are aware of the benefits and the case studies. What they don’t know is how to apply this knowledge to their own journey so their IoT story isn’t one of overbearing complexity and rising costs.

Another stumbling block is perception. Yes, there is the futuristic potential with the talking fridge and intelligent desk, but this is not where the real value lies. Organisations are overlooking the challenges that can be solved by realistic IoT, the banal and the boring solutions that leverage systems to deliver on business priorities. IoT’s potential sits within its ability to get the best out of assets and production efficiencies, solving problems in automation, security, and environment.

In addition to this, there is a lack of clarity around return on investment, uncertainty around the benefits, a lack of executive leadership, and concerns around security and the complexities of regulation.  Because IoT is an emerging technology there remains a limited awareness of the true extent of its value proposition and yet 66% of organisations are confident that this value exists.

This percentage poses both a problem and opportunity. On one hand, it showcases the local shift in thinking towards IoT as a technology worth investing into. On the other hand, many companies are seeing the competition invest and leaping blindly in the wrong direction. Stop. IoT is not the same for every business.

It is essential that every company makes its own case for IoT based on its needs and outcomes. Does agriculture have the same challenges as mining? Does one mining company have the same challenges as another? The answer is no. Organisations that want their IoT investment to succeed must reject the idea that they can pick up where another has left off. IoT must be relevant to the business outcome that it needs to achieve. While some use cases may apply to most industries based on specific circumstances, there are different realities and priorities that will demand a different approach and starting point.

Ask – what is the business problem right now and how can technology be leveraged to resolve it?

In the agriculture space, there is a need to improve crop yields and livestock management, improve farm productivity and implement environmental monitoring. In the construction and mining industry, safety and emergency response are a priority alongside workforce and production management. Education shifts the lens towards improving delivery and quality of education, access to advanced learning methods and reducing the costs of learning.  Smart cities want to improve traffic and efficiently deliver public services and healthcare is focusing on wellness, reducing hospital admissions and the security of assets and inventory management.

The technology and solutions selected must speak to these specific challenges.

If there are no insights used to create an IoT solution, it’s the equivalent of having the fastest Ferrari on Rivonia Road in peak traffic. It makes a fantastic noise, but it isn’t going to move any faster than the broken-down sedan in the next lane. Everyone will be impressed with the Ferrari, but the amount of power and the size of the investment mean nothing. It’s in the wrong place.

What differentiates the IoT successes is how a company leverages data to deliver meaningful value-added predictions and actions for personalised efficiencies, convenience, and improved industry processes. To move forward the organisation needs to focus on the business outcomes and not just the technology. They need to localise and adapt by applying context to the problem that’s being solved and explore innovation through partnerships and experimentation.

Continue Reading

Featured

ERP underpins food tracking

The food traceability market is expected to reach almost $20 billion by 2022 as increased consumer awareness, strict governance requirements, and advances in technology are resulting in growing standardisation of the segment, says STUART SCANLON, managing director of epic ERP

Published

on

Just like any data-driven environment, one of the biggest enablers of this is integrated enterprise resource planning (ERP) solutions.

As the name suggests, traceability is the ability to track something through all stages of production, processing, and distribution. When it comes to the food industry, traceability must also enable stakeholders to identify the source of all food inputs that can include anything from raw materials, additives, ingredients, and packaging.

Considering the wealth of data that all these facets generate, it is hardly surprising that systems and processes need to be put in place to manage, analyse, and provide actionable insights. With traceability enabling corrective measures to be taken (think product recalls), having an efficient system is often the difference between life or death when it comes to public health risks.

Expansive solutions

Sceptics argue that traceability simply requires an extensive data warehouse to be done correctly, the reality is quite different. Yes, there are standard data records to be managed, but the real value lies in how all these components are tied together.

ERP provides the digital glue to enable this. With each stakeholder audience requiring different aspects of traceability (and compliance), it is essential for the producer, distributor, and every other organisation in the supply chain, to manage this effectively in a standardised manner.

With so many different companies involved in the food cycle, many using their own, proprietary systems, just consider the complexity of trying to manage traceability. Organisations must not only contend with local challenges, but global ones as well as the import and export of food are big business drivers.

So, even though traceability is vital to keep track of everything in this complex cycle, it is also imperative to monitor the ingredients and factories where items are produced. Having expansive solutions that must track the entire process from ‘cradle to grave’ is an imperative. Not only is this vital from a safety perspective, but from cost and reputational management aspects as well. Just think of the recent listeriosis issue in South Africa and the impact it has had on all parties in that supply chain.

Efficiency improvements

Thanks to the increasing digital transformation efforts by companies in the food industry, traceability becomes a more effective process. It is no longer a case of using on-premise solutions that can be compromised but having hosted ones that provide more effective fail-safes.

In a market segment that requires strict compliance and regulatory requirements to be met, cloud-based solutions can provide everyone in the supply chain with a more secure (and tamper-resistant) solution than many of the legacy approaches of old.

This is not to say ERP requires the one or the other. Instead, there needs to be a transition provided between the two scenarios that empowers those in the food supply chain to maximise the insights (and benefits) derived from traceability.

Now, more than ever, traceability is a business priority. Having the correct foundation through effective ERP is essential if a business can manage its growth and meet legislative requirements into the future.

Continue Reading

Trending

Copyright © 2018 World Wide Worx