Data management choice has a determining impact on your core business processes, which can now rely on real-time insights from data at each decision point, improving productivity and empowering informed decision-making.
However, implementing an ERP project impacts and demands changes in all your other data management projects, such as reporting, analytics, IoT, enterprise information management, data governance, Big Data, custom innovation projects, or cloud adoption.
According to Bernd Leukert, member of the SAP Executive Board, real-time data is one of the central components of the intelligent enterprise. Data is the currency of the digital transformation, and data-driven applications are the future of application development. An end-to-end process opens up a whole new world of data-driven decision-making when it can collect sensor data to provide new pay-as-you-go business models, for example, while incorporating spatial analytics, machine learning, blockchain, contracting, operations, and billing.
Complex data landscapes present challenges
As data landscapes become increasingly complex, companies need a solution that simplifies their landscape while also ensuring the highest levels of security. To become a successful digital business, you need to choose a data management strategy that empowers you to connect all your company’s data assets and analyze them in context to get the full picture of your business. Only when you achieve real-time situational awareness can you truly act in the moment, predict future business outcomes with confidence, and devise new business models that can propel your business forward.
A winning strategy addresses three critical imperatives:
- Trusted data: Every business faces the challenge of managing the influx of both structured and unstructured data from multiple applications, files, databases, data warehouses, and data lakes. To empower your company to share data freely and extract meaningful value from it, your data management solution needs to deliver ubiquitous data access while protecting the security, privacy, and integrity of the data at all times. Only when these conditions are met can your company trust its data.
- Connected, intelligent data: With data being produced in large quantities by disparate data sources everywhere inside and outside the enterprise, gaining real-time situational awareness is becoming a daunting task. When you want to productize new data scenarios to gain a comprehensive source-agnostic overview of the business, you have to combine diverse sets of data – whether Big Data, transactional data, or analytical data – into a single data universe. The data management solution needs to provide the ability to easily connect all this data while avoiding unnecessary data duplications and movements. You can take advantage of your connected data universe and extract intelligence from it.
- Architecture flexibility that embraces the cloud: A business must retain the flexibility to decide where to store and process its data. The data management solution needs to ensure that you can store and process data in any cloud and on hybrid (cloud/on-premises) environments with no lock-in. Only when these criteria are met can you ensure that business priorities drive your technology choices instead of the other way around.
Colgate-Palmolive success story
Take the example of Colgate-Palmolive. This company has been able to innovate and leverage the value of data at a much faster pace, expanding the company’s opportunity to drive growth.
As its CEO Mike Crowe said:
“The goal is to move from periodic structured reviews to be able to review the state of the business at any time in real time, using the latest data that allows us to identify issues faster and work together to get a solution sooner … We want to simplify our computing environment. We want it as efficient as possible. Because we need to free up time to work in what many people call ‘mode 2’– exploration: looking for new opportunities that can drive our growth.”
Business needs more trusted data; clearly, this is true. But what are the challenges to managing data with care and diligence as you would any other key asset? Data shows its value when it gets used, when it moves from A to B, as it flows. Consider data as the new “currency of business.”
As with money, there can be multiple end points and intermediaries that need visibility to assure you that the money is from reliable sources, not double-counted, lost, or even counterfeit.
Similarly, these concerns in the cash example also reflect the demands for data security and privacy when the data is flowing. For example, you need to safeguard access to data and conceal people’s identities to avoid unlawful identification. Trusted data should be accessed only by people and machines with the right credentials in order to respect the privacy of the individuals the data represents.
In other words, you need to trust your data. If the underlying data can’t be trusted, you will soon run into issues. In enterprise terms, this rapidly moves into a discussion around data governance.
Even if you have been working a long time to properly manage data – its quality and lineage – with respected stewards and custodians across your enterprise, there are some new challenges emerging:
- Data protection and consent for its use, demanded by citizens and consumers, and enforced by regulations such as GDPR, are new attributes impacting the value and quality of data.
- Change adds yet another dimension. Gone are the days of a static landscape with defined data flows between established processes inside the corporate firewall.
Today, business is highly flexible, with new data sources and an ever-increasing number of data consumers inside and outside the organization. There is a huge increase both in the supply and the demand for data. Trust in data also means establishing visibility in end points and “intermediaries” with broader visibility and cooperation across systems.
To achieve trust with data, you need a holistic, flexible, and open platform that works in real time. The following steps enable you to meet your expanding data management requirements:
- Establishing a trusted data-governance foundation: Define and document data and security policies, business rules, master data definitions, business terminology standards, and enterprise architecture models. Discover and document where data and content resides. Identify and document what data and content is subject to which internal policies and external regulatory requirements
- Driving business trust with always-accurate data: Cleanse, match, consolidate, and enrich data to comply with corporate standards. Continually monitor and measure data quality against validation rules, display metrics scorecards, and quantify the financial impact of poor quality. Manage and synchronize master data across applications
- Setting “trust” as a priority across the whole data lifecycle: Maintain the system by managing the content lifecycle and its association with data, processes, and applications. Implement access controls, data anonymization, and encryption. Establish and communicate data owners and stewards. Embed validation checks into business process and data-entry workflow. Implement archiving, retention, and deletion policies and rules
- Centralize landscape orchestration and governance, metadata management, and lifecycle management. Prepare data as it is being ingested, govern data across all data stores (whether on-premises or in the cloud), and define data flows for machine learning models.
Connected, intelligent data
For connected and intelligent data, you need three core advanced capabilities: 1) data access, 2) analytics processing, and 3) application development.
1. Data access
Gain a complete and accurate view of your business by accessing data from any source – internal or external.
- Access data where it’s located, integrate or replicate relevant data, and ensure data quality to increase confidence in decision making.
- Enhance, cleanse, and transform data to make the data more accurate and useful with a simplified landscape – one environment in which to provision and consume data. This also allows accessing more data formats, including an open framework for new data sources.
- Take advantage of logical data models and data virtualization capabilities to query data wherever it resides. You can retain the ability to ingest high-value data from various sources with smart data-integration capabilities.
2. Analytics processing
Gain new insights from advanced analytics processing by leveraging in-memory data processing capabilities (text, predictive, spatial, graph, streaming, and time series). You can get answers to any business question and make smart decisions in real time.
- The intelligent business can get new insights from business data enriched with geospatial data (read more). Discover relationships on the fly from connected graph data, operate on live data for real-time insight, and seek out new opportunities or optimize decisions based on the predictive value of large-scale data sets (with in-database machine learning algorithms with major machine learning frameworks such as Tensorflow and R.)
- The streaming techniques can help you capture, filter, analyze, and act on millions of events per second in real-time from a wide variety of sources.
3. Application development
Develop next-generation applications that combine analytics and transactions and deploy them on any device.
- Use various open-source development tools, such as Git, GitHub, and Apache Maven. Additionally, build enterprise-class non-SQL (NoSQL) applications with the support to store schema-flexible data in JSON format. Combine JSON data with structured data and query or analyze it using SQL.
Architecture flexibility and the cloud
There is also no one recipe for cloud adoption. Companies are experimenting and adopting the cloud at different paces, and for carrying on both core business processes and developing new innovations – as they pursue a common quest for greater business agility and lower costs. To meet different goals for cloud and architecture flexibility, businesses need the freedom of movement of data and queries spanning local, public, and private clouds without cloud-vendor lock-in to maximize the deployment flexibility.
Moreover, in a digital enterprise, the vast majority of new data will be generated outside enterprise data centers – in the cloud and at the edge of the network – by sensors and a multitude of devices. With companies increasingly relying on data to transform their business and achieve new levels of productivity, choosing a data management strategy that includes the cloud is paramount. The new data management strategy should allow a company to:
- Retain the freedom to decide where to deploy its applications and data, which is about the flexibility of cloud option and architecture
- Adopt the cloud at the pace dictated by the company’s unique business priorities
Another side of the story is the complexity of business IT infrastructure. The intelligent enterprise needs a complete solution that can be easily customized to deliver logical architectures for different business needs. So the same core and components can scale from a data mart scenario to a data lake scenario while handling streaming data, or expand it to a data-cleansing scenario to clean the data during the data-loading process.
Then add freedom of movement and synchronization of data, instances, and queries spanning local, private, and multi-public clouds with architecture flexibility – and the cloud flexibility to enable users to meet any requirements to orchestrate all the data you need into a trusted, unified landscape.
To learn more, listen to a replay of the May 2 Webcast “Deliver Secure Data Management and Complete Insights with SAP S/4HANA.”